Coffee intake and risk of diabetic retinopathy: a Mendelian randomization study

doi:10.21203/rs.3.rs-4718773/v1

Download PDF

Article

Coffee intake and risk of diabetic retinopathy: a Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4718773/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Observational studies have reported a link between coffee consumption and diabetic retinopathy (DR), but the causal relationship remains a subject of debate. To explore these causal associations, we conducted a Mendelian randomization (MR) study.

Methods: We utilized seven publicly accessible genome-wide association studies (GWAS) datasets related to coffee intake (ukb-b-5237) and DR (finn-b-DM_RETINOPATHY, finn-b-H7_RETINOPATHYDIAB, finn-b-DM_RETINOPATHY_EXMORE, finn-b-DM_RETINA_PROLIF, finn-b-H7_RETINOPATHYDIAB_PROLIF, and finn-b-H7_RETINOPATHYDIAB_BKG). MR analyses were performed using inverse variance weighted (IVW), weighted median, MR-Egger regression, and weighted mode estimates.

Results: Employing 40 single-nucleotide polymorphisms (SNPs) of genome-wide significance as instrumental variables for coffee intake, our study revealed a positive correlation between genetically predicted coffee consumption and the risk of DR (OR= 1.39, 95% CI: 1.02 to 1.90, p = 0.037; OR=2.25, 95% CI: 1.21 to 4.20, p = 0.011; OR= 1.66, 95% CI: 1.17 to 2.35, p = 0.004; OR= 1.75, 95% CI: 1.20 to 2.56, p = 0.004; OR= 2.79, 95% CI: 1.08 to 7.23, p = 0.035; OR= 2.48, 95% CI: 1.16 to 5.29, p = 0.019). These results were consistent across various sensitivity analyses and free from pleiotropy.

Conclusions: Our findings indicate a positive association between coffee intake and the risk of DR, as determined by genetic data. These correlations warrant serious consideration in clinical settings.

Health sciences/Endocrinology/Endocrine system and metabolic diseases

Health sciences/Diseases/Endocrine system and metabolic diseases

Health sciences/Diseases/Eye diseases

Health sciences/Risk factors

Health sciences/Medical research/Genetics research

coffee intake

diabetic retinopathy (DR)

Mendelian randomization (MR)

single-nucleotide polymorphisms (SNPs)

Mendelian randomization (MR).

Diabetes stands as one of the top non-communicable diseases worldwide and is a significant contributor to global mortality rates(1). The International Diabetes Federation's 2021 report indicates that diabetes impacts 537 million people, or 10.5% of the global population, with projections estimating this number to rise to 783 million by 2045. The financial implications are considerable, with expenditures reaching at least $966 billion in 2021, reflecting a 316% increase over the past decade and a half (2).Diabetic retinopathy (DR), a prevalent complication of diabetes, affects approximately 30 to 40% of those with the condition(3, 4). Presently, over 100 million people globally are living with DR, which is a primary cause of blindness and visual impairment, particularly among adults in the workforce (3, 5). The prevalence and burden of DR are predicted to escalate dramatically in the coming decades, with numbers expected to jump from 103 million in 2020 to 130 million by 2030 and 161 million by 2045(6). This escalation is attributed to the growing incidence of diabetes, shifts in lifestyle, and the expanding aging population(6). The anticipated 25% increase in DR's disease burden within a decade is poised to place additional pressure on healthcare systems that are already under strain. The economic impact of DR, including its complications, is significant, with direct healthcare costs for vision loss in the United States alone amounting to $134.2 million in 2017(7).

Coffee, ranking just behind water in global popularity, boasts an estimated market value of $10 billion. This widely enjoyed drink is a rich source of various bioactive compounds, including chlorogenic acid, caffeine, and diterpenes. These components are known for their multifaceted health benefits, such as antioxidant properties(8), anti-inflammatory effects(9), chemo-preventive actions(10), modulation of glucose and fat metabolism(11, 12), enhancement of gut microbiome health(13), and other biological activities(14–16). Epidemiological research indicates an inverse relationship between coffee consumption and the risk of insulin resistance and type 2 diabetes (17–21), suggesting that coffee may offer long-term, dose-dependent protective effects. The potential mechanisms behind these benefits could include the acarbose-like activity of coffee phenolics(17), which may inhibit glucose absorption by altering the expression of glucose transporters like GLUT-2 and SGLT-1(18). Additionally, chlorogenic acids (CGAs) and their metabolites might stimulate insulin synthesis, enhance insulin secretion, and ameliorate insulin resistance by influencing plasma levels of glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP)(19).

The observable retinal abnormalities in DR, including microaneurysms, hemorrhages, and hard exudates, stem mainly from damage to the retinal microvasculature. Optical coherence tomography (OCT) has revealed significant thinning of the inner retinal layers in diabetic patients, notably the retinal nerve fiber layer (RNFL) and the ganglion cell layer (GCL), a process that is progressive and may occur before the emergence of overt DR lesions (20, 21). This thinning is further supported by histological evidence from enucleated eyes, which show decreased retinal ganglion cell density in cases of DR (22). Functional evaluations in diabetic patients have uncovered diminished contrast sensitivity, visual field impairments, electrophysiological deficits, and weakened pupillary responses(23). Observational research has linked higher coffee consumption with a thicker macular RNFL and a reduced risk of hypoxia-induced retinal degeneration(24, 25), as well as a decreased likelihood of developing diabetic retinopathy and cataract(26, 27). However, some studies have reported that coffee consumption could negatively affect glucose tolerance and insulin sensitivity(28, 29), raise intraocular pressure, and decrease blood flow to the macula, optic nerve head, and choroid(30, 31). Yet, other research has not found a connection between coffee intake and retinopathy(32). In conclusion, the relationship between coffee intake and DR lacks a consistent pattern, making it difficult to draw a definitive conclusion about the impact of coffee on the development or progression of DR.

Mendelian randomization (MR) is a valuable research technique that investigates causality by leveraging the random assortment of genetic variants related to a risk factor (33). These genetic variants act as a natural stand-in for exposure, creating an experiment-like scenario to determine causal relationships. By doing so, MR significantly reduces the influence of confounding variables, reverse causality, and measurement errors, providing stronger evidence for causality than traditional epidemiological methods(34). The effectiveness of MR is particularly notable when conventional methods for controlling confounding factors are insufficient, as it helps to avoid confounding biases and enhances the credibility of causality assessments.

In our study, we sought to examine the genetic causality between coffee consumption and DR using a two-sample MR analysis based on data from the Genome-Wide Association Studies (GWAS) database. This method estimates the causal relationship between a biomarker, such as coffee intake, and a disease, like DR, by utilizing genetic variants that are reliably associated with the exposure of interest. By applying two-sample MR and utilizing summary statistics from extensive GWAS on coffee intake and diabetes-related conditions, our study aimed to uncover the causal impact of coffee consumption on the risk of developing DR.

Study design description

A schematic of our two-sample Mendelian randomization (MR) approach is depicted in Fig. 1. We conducted two MR analyses using data from seven genome-wide association studies (GWAS) to explore the relationship between coffee consumption and diabetic retinopathy (DR). In these analyses, coffee intake was considered the exposure variable, while DR was the outcome of interest. The foundational assumptions of MR are outlined in Fig. 1. The data was sourced from the ieu open GWAS project, a public repository of genetic associations derived from GWAS summary datasets. Given the public availability of the data, this study did not necessitate informed consent or ethical approval, and there was no risk of individual identity disclosure.

Selection of instrumental variables for MR analyses

We carefully selected suitable instrumental variables for the MR analyses from two distinct GWAS summary results. Single nucleotide polymorphisms (SNPs) were identified as reliable (p < 5 × 10^− 8) and independent (r² < 0.001, with 10,000 kb between SNPs). SNPs that showed association with the outcomes with p < 5 × 10^− 8 were excluded. During the harmonization of exposure and outcome data, palindromic SNPs with intermediate allele frequencies were omitted. To address potential confounders of the outcomes, we utilized PhenoScanner V2 to eliminate related SNPs. The strength of the instruments was appraised using the F-statistics, with F < 10 suggesting weak instrument strength. The study incorporated seven publicly accessible GWAS datasets, namely coffee intake (ukb-b-5237), DR (finn-b-DM_RETINOPATHY, finn-b-H7_RETINOPATHYDIAB, finn-b-DM_RETINOPATHY_EXMORE, finn-b-DM_RETINA_PROLIF, finn-b-H7_RETINOPATHYDIAB_PROLIF, and finn-b-H7_RETINOPATHYDIAB_BKG). GWAS (ukb-b-5237) includes 428,860 participants and 40 SNPs were selected as instrumental variables associated with coffee intake (p < 5 × 10 − 8 and r² < 0.001). DR was defined as outcome met the ICD-10 criterion, with summary statistics including datasets namely finn-b-DM_RETINOPATHY (14,584 cases and 176,010 controls), finn-b-H7_RETINOPATHYDIAB (3,646 cases and 203,018 controls), finn-b-DM_RETINOPATHY_EXMORE (14,584 cases and 176,010 controls), finn-b-DM_RETINA_PROLIF (8,681 cases and 204,208 controls), finn-b-H7_RETINOPATHYDIAB_PROLIF (1,382 cases and 203,018 controls), and finn-b-H7_RETINOPATHYDIAB_BKG (2,026 cases and 203,018 controls).

Statistical analysis

The primary statistical method employed was the inverse-variance weighted (IVW) approach to estimate potential causal associations. To ensure accuracy in the presence of invalid SNPs, we also applied MR-Egger (34) and weighted median (35) methods. The proportion of variance in the exposure variable explained by the genetic variants was quantified using R². The F-statistics were calculated to assess the instrument strength, with F < 10 indicating weak strength (36). Horizontal pleiotropy was assessed using the MR-PRESSO global test (37), and outliers were excluded accordingly. Heterogeneity was tested for IVW and MR-Egger methods using Cochran's Q statistics and funnel plots (38). Sensitivity analyses, including leave-one-out and single SNP analyses, were conducted to determine the influence of individual SNPs on the main causal relationship (39). Power calculations were performed using an online tool(https://shiny.cnsgenomics.com/mRnd/).

For binary outcomes, odds ratios (OR) and 95% confidence intervals (CI) were used to estimate the causal relationship's magnitude. The causal estimates, p-values, β coefficients, and their standard errors were presented for both binary and continuous outcomes. All p-values were two-tailed. The analyses were conducted using the TwoSampleMR (version 0.6.2) packages in R (version 4.3.1). The overall workflow of the two-sample MR study is illustrated in Fig. 2.

$$\:F=\left(\frac{{R}^{2}}{1-{R}^{2}}\right)\times\:\left(\frac{n-k-1}{k}\right)$$

$$\:{R}^{2}=2\times\:\left(1-MAF\right)\times\:MAF\times\:{\beta\:}^{2}$$

The casual effect of coffee intake on DR

The study's findings regarding the impact of coffee intake on diabetic retinopathy (DR) are illustrated in Fig. 4. The odds ratios (ORs) from the inverse-variance weighted (IVW) analysis for each outcome are displayed in forest plots. The causal effect estimates for each SNP are graphically represented in various formats: scatter plots in Fig. 2, forest plots in Fig. 3, leave-one-out plots in Supplementary Fig. 1, and funnel plots in Supplementary Fig. 2. Key findings indicate a positive correlation between genetically predicted coffee intake and the risk of DR (OR = 1.39, 95% CI: 1.02 to 1.90, p = 0.037; OR = 2.25, 95% CI: 1.21 to 4.20, p = 0.011; OR = 1.66, 95% CI: 1.17 to 2.35, p = 0.004; OR = 1.75, 95% CI: 1.20 to 2.56, p = 0.004; OR = 2.79, 95% CI: 1.08 to 7.23, p = 0.035; OR = 2.48, 95% CI: 1.16 to 5.29, p = 0.019). These results were consistent across multiple sensitivity analyses and showed no pleiotropy.

Finn-b-DM_RETINOPATHY

The IVW analysis revealed an OR of 1.39 (OR = 1.39, 95% CI: 1.02 to 1.90, p = 0.037) for DR. The MR-Egger (OR = 2.05, P = 0.028), and Weighted Median (OR = 1.67, P = 0.018) results were consistent with the IVW analysis. No heterogeneity was detected in Cochran’s Q test (MR-Egger P = 0.368 and IVW P = 0.323), and neither the MR-Egger (regression intercept P = 0.163) nor the MR-PRESSO (Global test P = 0.322) tests indicated horizontal pleiotropy. The leave-one-out sensitivity analysis validated the robustness of this finding (Fig. 4 and Figure S1A).

Finn-b-H7_RETINOPATHYDIAB

The IVW analysis revealed an OR of 2.25 (OR = 2.25, 95% CI: 1.21 to 4.20, p = 0.011) for DR. The Weighted Median (OR = 2.35, P = 0.05) result was consistent with the IVW analysis. No heterogeneity was detected in Cochran’s Q test (MR-Egger P = 0.212 and IVW P = 0.247), and neither the MR-Egger (regression intercept P = 0.959) nor the MR-PRESSO (Global test P = 0.244) tests indicated horizontal pleiotropy. The leave-one-out sensitivity analysis validated the robustness of this finding (Fig. 4 and Figure S1B).

Finn-b-DM_RETINOPATHY_EXMORE

The IVW analysis revealed an OR of 1.66 (OR = 1.66, 95% CI: 1.17 to 2.35, p = 0.004) for DR. The MR-Egger (OR = 2.34, P = 0.022), Weighted Median (OR = 1.75, P = 0.013), and Weighted Mode (OR = 1.72, P = 0.025) results were consistent with the IVW analysis. No heterogeneity was detected in Cochran’s Q test (MR-Egger P = 0.113 and IVW P = 0.104), and neither the MR-Egger (regression intercept P = 0.269) nor the MR-PRESSO (Global test P = 0.115) tests indicated horizontal pleiotropy. The leave-one-out sensitivity analysis validated the robustness of this finding (Fig. 4 and Figure S1C).

Finn-b-DM_RETINA_PROLIF

The IVW analysis revealed an OR of 1.75 (OR = 1.75, 95% CI: 1.20 to 2.56, p = 0.004) for DR. The Weighted Median (OR = 1.68, P = 0.05) result was consistent with the IVW analysis. No heterogeneity was detected in Cochran’s Q test (MR-Egger P = 0.585 and IVW P = 0.619), and neither the MR-Egger (regression intercept P = 0.626) nor the MR-PRESSO (Global test P = 0.647) tests indicated horizontal pleiotropy. The leave-one-out sensitivity analysis validated the robustness of this finding (Fig. 4 and Figure S1D).

Finn-b-H7_RETINOPATHYDIAB_PROLIF

The IVW analysis revealed an OR of 2.79 (OR = 2.79, 95% CI: 1.08 to 7.23, p = 0.035) for DR. No heterogeneity was detected in Cochran’s Q test (MR-Egger P = 0.310 and IVW p = 0.348), and neither the MR-Egger (regression intercept P = 0.766) nor the MR-PRESSO (Global test P = 0.414) tests indicated horizontal pleiotropy. The leave-one-out sensitivity analysis validated the robustness of this finding (Fig. 4 and Figure S1E).

Finn-b-H7_RETINOPATHYDIAB_BKG

The IVW analysis revealed an OR of 2.48 (OR = 2.48, 95% CI: 1.16 to 5.29, p = 0.019) for DR. The MR-Egger (OR = 5.57, P = 0.034), and Weighted Mode (OR = 3.13, P = 0.05) results were consistent with the IVW analysis. No heterogeneity was detected in Cochran’s Q test (MR-Egger P = 0.555 and IVW p = 0.534), and neither the MR-Egger (regression intercept P = 0.239) nor the MR-PRESSO (Global test P = 0.529) tests indicated horizontal pleiotropy. The leave-one-out sensitivity analysis validated the robustness of this finding (Fig. 4 and Figure S1F).

In this scholarly research, we undertook a two-sample Mendelian randomization (MR) analysis to thoroughly evaluate the potential causal link between coffee consumption and diabetic retinopathy (DR). Our results indicate the presence of a causal relationship between the intake of coffee and the increased risks of DR.

The relationship between coffee consumption and DR has been a subject of debate across various research methodologies, encompassing epidemiological studies, animal and cellular experiments, meta-analyses, prospective studies, randomized controlled trials, and MR studies. Our MR study aligns with the consensus found in most MR research. For example, Zhang et al. identified a genetic causal link between the intake of artificial sweeteners from coffee and type 2 diabetes mellitus (T2DM) (OR = 1.265, 95% CI: 1.035 to 1.545, P = 0.021) (40). Similarly, Yuan et al. discovered 19 risk factors causally associated with T2DM, including coffee or caffeine consumption (OR = 1.59, 95% CI:1.09 to 2.32, P = 0.027) (41). Wang et al. reported that total coffee intake was associated with an increased risk of T2DM and body mass index (BMI), with beta coefficients of 0.31 (P = 0.033) and 0.36 (P = 1.8×10^− 5), respectively, even after adjusting for BMI (42). Nordestgaard et al., in an MR study, indicated that increased coffee intake correlates with higher levels of BMI, waist circumference, weight, systolic and diastolic blood pressure, plasma triglycerides, and total cholesterol—all factors closely related to insulin resistance and lipid metabolism(43), findings that are in line with Kwok's research(44). Additionally, an MR study has suggested that coffee consumption is associated with an increased risk of primary open-angle glaucoma (OR = 1.241, P = 0.016)(30). In individuals who are not regular caffeine consumers, a daily intake exceeding 480 mg was found to be correlated with a statistically significant increase of 0.35 mmHg in intraocular pressure and a 3.90-fold increase in glaucoma prevalence (P = 0.01 and P = 0.0003, respectively)(45).

The findings underscore the potential adverse health effects associated with high coffee consumption, especially concerning the risk of glaucoma and blood sugar regulation. Several mechanisms may account for these effects: (1) Caffeine, as noted by Naim et al., can elevate vascular resistance and decrease blood flow in the optic nerve and retrobulbar vessels, which could be a factor in glaucoma development(46); (2) Caffeine's role as a phosphodiesterase inhibitor may lead to increased intracellular cyclic AMP levels, which in turn can stimulate the ciliary body to produce more aqueous humor. Additionally, caffeine exposure has been linked to ultrastructural changes in the non-pigmented ciliary epithelium that could facilitate aqueous transport(47); (3) The high intake of refined sugars or coffee mate, common additives in coffee drinks, may over time raise blood sugar levels and cause hyperkalemia. Prolonged exposure to these conditions can impact renal interstitial fibrosis and blood pressure, contribute to oxidative stress, and lead to nutritional imbalances(48). These mechanisms suggest that while coffee has many health benefits, excessive consumption could have detrimental effects on health, particularly in the context of eye health and blood sugar management.

Our study boasts several strengths, such as its substantial sample size and the utilization of two-sample MR to elucidate the genetic underpinnings of the relationship between coffee consumption and DR. The genetic analysis tools we employed were also reliable, with a strong statistical power (F-statistic > 10). Our results highlight the need for deeper exploration into the inconsistencies between observational data and the absence of definitive causal evidence. This discrepancy might be due to residual confounding factors or reverse causation in observational studies, which are challenging to manage completely, such as the potential influence of BMI on the connection between coffee intake and DR risk.

However, our MR study is not without its limitations. Firstly, the lack of Genome-Wide Association Study (GWAS) data on coffee preparation techniques, varieties of coffee, and specific consumption levels precluded us from assessing the causal impacts of these variables. Secondly, the study's demographic was exclusively European, which might restrict the generalizability of our findings to other ethnic groups. Thirdly, despite a sizable sample, the number of DR cases was comparatively low, which could have reduced the study's statistical power to identify significant differences. Lastly, the absence of specific coffee consumption data in the GWAS dataset leaves open the possibility that very high or very low caffeine intake levels might affect the risk of DR in ways we could not ascertain.

The study discovered a positive link between the consumption of coffee and the likelihood of developing diabetic retinopathy (DR), based on an analysis of genetic data. These findings warrant serious consideration in clinical settings. To reach a more precise and reliable conclusion, further research is necessary. This includes large-scale, prospective, randomized controlled trials, as well as subgroup analyses that focus on factors such as the method of coffee preparation, the type of coffee consumed, and the quantity of coffee intake.

Data availability

Data are available in a public, open access repository. See: https://gwas.mrcieu.ac.uk/datasets.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

XH conceived the study, collected data, performed the statistical analysis, and participated in writing and preparation of the report. YYW analyzed the data and revised the report. YLN was involved in the data collection, interpretation of the data and preparation of the report. XH assumes full responsibility for the overall content of this report.

Funding

The author(s) received a financial support for the research, authorship, and/or publication of this article, named Zhejiang Province Medical and Health Technology Program (2024KY1856).

Organization WH. Global action plan for the prevention and control of noncommunicable diseases: 2013–2020. 2020. doi:http://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf
Federation ID. IDF Diabetes Atlas 2021–10th edition. 2021. doi:
Yau JW, Rogers SL, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556–64. doi:10.2337/dc11-1909
Tan TE, Wong TY. Diabetic retinopathy: Looking forward to 2030. Front Endocrinol (Lausanne). 2022;13:1077669. doi:10.3389/fendo.2022.1077669
Blindness GBD, Vision Impairment C, Vision Loss Expert Group of the Global Burden of Disease S. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9(2):e144-e60. doi:10.1016/S2214-109X(20)30489-7
Teo ZL, Tham YC, Yu M, et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology. 2021;128(11):1580–91. doi:10.1016/j.ophtha.2021.04.027
Rein DB, Wittenborn JS, Zhang P, et al. The Economic Burden of Vision Loss and Blindness in the United States. Ophthalmology. 2022;129(4):369–78. doi:10.1016/j.ophtha.2021.09.010
Correa TA, Monteiro MP, Mendes TM, et al. Medium light and medium roast paper-filtered coffee increased antioxidant capacity in healthy volunteers: results of a randomized trial. Plant Foods Hum Nutr. 2012;67(3):277–82. doi:10.1007/s11130-012-0297-x
Ochoa-Rosales C, van der Schaft N, Braun KVE, et al. C-reactive protein partially mediates the inverse association between coffee consumption and risk of type 2 diabetes: The UK Biobank and the Rotterdam study cohorts. Clin Nutr. 2023;42(5):661–9. doi:10.1016/j.clnu.2023.02.024
van Dam RM, Hu FB, Willett WC. Coffee, Caffeine, and Health. N Engl J Med. 2020;383(4):369–78. doi:10.1056/NEJMra1816604
Vitaglione P, Morisco F, Mazzone G, et al. Coffee reduces liver damage in a rat model of steatohepatitis: the underlying mechanisms and the role of polyphenols and melanoidins. Hepatology. 2010;52(5):1652–61. doi:10.1002/hep.23902
Lecoultre V, Carrel G, Egli L, et al. Coffee consumption attenuates short-term fructose-induced liver insulin resistance in healthy men. Am J Clin Nutr. 2014;99(2):268–75. doi:10.3945/ajcn.113.069526
Jaquet M, Rochat I, Moulin J, Cavin C, Bibiloni R. Impact of coffee consumption on the gut microbiota: a human volunteer study. Int J Food Microbiol. 2009;130(2):117–21. doi:10.1016/j.ijfoodmicro.2009.01.011
Barrea L, Pugliese G, Frias-Toral E, et al. Coffee consumption, health benefits and side effects: a narrative review and update for dietitians and nutritionists. Crit Rev Food Sci Nutr. 2023;63(9):1238–61. doi:10.1080/10408398.2021.1963207
Giuseppe Grosso JG, Fabio Galvano, and Edward L. Giovannucci. Coffee, Caffeine, and Health Outcomes: An Umbrella Review. Annual Review of Nutrition. 2017;21:25. doi:10.1146/annurev-nutr-071816-064941
Nieber K. The Impact of Coffee on Health. Planta Med. 2017;83(16):1256–63. doi:10.1055/s-0043-115007
Nyambe-Silavwe H, Williamson G. Chlorogenic and phenolic acids are only very weak inhibitors of human salivary alpha-amylase and rat intestinal maltase activities. Food Res Int. 2018;113:452–5. doi:10.1016/j.foodres.2018.07.038
Gauer JS, Tumova S, Lippiat JD, Kerimi A, Williamson G. Differential patterns of inhibition of the sugar transporters GLUT2, GLUT5 and GLUT7 by flavonoids. Biochem Pharmacol. 2018;152:11–20. doi:10.1016/j.bcp.2018.03.011
Williamson G, Clifford MN. Role of the small intestine, colon and microbiota in determining the metabolic fate of polyphenols. Biochem Pharmacol. 2017;139:24–39. doi:10.1016/j.bcp.2017.03.012
Lim HB, Shin YI, Lee MW, Park GS, Kim JY. Longitudinal Changes in the Peripapillary Retinal Nerve Fiber Layer Thickness of Patients With Type 2 Diabetes. JAMA Ophthalmology. 2019;137(10). doi:10.1001/jamaophthalmol.2019.2537
Sohn EH, van Dijk HW, Jiao C, et al. Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. Proceedings of the National Academy of Sciences. 2016;113(19). doi:10.1073/pnas.1522014113
Obara EA, Hannibal J, Heegaard S, Fahrenkrug J. Loss of Melanopsin-Expressing Retinal Ganglion Cells in Patients With Diabetic Retinopathy. Investigative Opthalmology & Visual Science. 2017;58(4). doi:10.1167/iovs.16-21168
Tan TE, Finkelstein MT, Tan GSW, et al. Retinal neural dysfunction in diabetes revealed with handheld chromatic pupillometry. Clin Exp Ophthalmol. 2022;50(7):745–56. doi:10.1111/ceo.14116
Jang H, Ahn HR, Jo H, et al. Chlorogenic acid and coffee prevent hypoxia-induced retinal degeneration. J Agric Food Chem. 2014;62(1):182–91. doi:10.1021/jf404285v
Jang H, Choi Y, Ahn HR, Jung SH, Lee CY. Effects of phenolic acid metabolites formed after chlorogenic acid consumption on retinal degeneration in vivo. Mol Nutr Food Res. 2015;59(10):1918–29. doi:10.1002/mnfr.201400897
Alcubierre N, Granado-Casas M, Bogdanov P, et al. Caffeine and the Risk of Diabetic Retinopathy in Type 2 Diabetes Mellitus: Findings from Clinical and Experimental Studies. Nutrients. 2023;15(5). doi:10.3390/nu15051169
Lee HJ, Park JI, Kwon SO, Hwang DD. Coffee consumption and diabetic retinopathy in adults with diabetes mellitus. Sci Rep. 2022;12(1):3547. doi:10.1038/s41598-022-07192-6
Greenberg JA, Owen DR, Geliebter A. Decaffeinated coffee and glucose metabolism in young men. Diabetes Care. 2010;33(2):278–80. doi:10.2337/dc09-1539
Lesley L Moisey SK, Andrea C Bickerton, Lindsay E Robinson, and Terry E Graham. Caffeinated coffee consumption impairs blood glucose homeostasis in response to high and low glycemic index meals in healthy men. The American Journal of Clinical Nutrition. 2008;87(5):7. doi:10.1093/ajcn/87.5.1254
Li X, Cheng S, Cheng J, Wang M, Zhong Y, Yu AY. Habitual Coffee Consumption Increases Risk of Primary Open-Angle Glaucoma: A Mendelian Randomization Study. Ophthalmology. 2022;129(9):1014–21. doi:10.1016/j.ophtha.2022.04.027
Nagasato D, Mitamura Y, Egawa M, et al. Changes in Choroidal Component Ratio and Circulation After Coffee Intake in Healthy Subjects. Invest Ophthalmol Vis Sci. 2021;62(3):27. doi:10.1167/iovs.62.3.27
Liu YJ, Miao MY, Wang JM, et al. Coffee Consumption and Incidence of Cardiovascular and Microvascular Diseases in Never-Smoking Adults with Type 2 Diabetes Mellitus. Nutrients. 2023;15(18). doi:10.3390/nu15183910
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. doi:10.1136/bmj.k601
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. doi:10.1093/ije/dyv080
Carter AR, Sanderson E, Hammerton G, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021;36(5):465–78. doi:10.1007/s10654-021-00757-1
Burgess S, Thompson SG, Collaboration CCG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–64. doi:10.1093/ije/dyr036
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. doi:10.1038/s41588-018-0099-7
Bowden J, Spiller W, Del Greco MF, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018;47(4):1264–78. doi:10.1093/ije/dyy101
Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377–89. doi:10.1007/s10654-017-0255-x
Zhang Y, Tang Z, Shi Y, Li L. Associations between artificial sweetener intake from cereals, coffee, and tea and the risk of type 2 diabetes mellitus: A genetic correlation, mediation, and mendelian randomization analysis. PLoS One. 2024;19(2):e0287496. doi:10.1371/journal.pone.0287496
Yuan S, Larsson SC. An atlas on risk factors for type 2 diabetes: a wide-angled Mendelian randomisation study. Diabetologia. 2020;63(11):2359–71. doi:10.1007/s00125-020-05253-x
Wang X, Jia J, Huang T. Coffee Types and Type 2 Diabetes Mellitus: Large-Scale Cross-Phenotype Association Study and Mendelian Randomization Analysis. Front Endocrinol (Lausanne). 2022;13:818831. doi:10.3389/fendo.2022.818831
Nordestgaard AT, Thomsen M, Nordestgaard BG. Coffee intake and risk of obesity, metabolic syndrome and type 2 diabetes: a Mendelian randomization study. Int J Epidemiol. 2015;44(2):551–65. doi:10.1093/ije/dyv083
Kwok MK, Leung GM, Schooling CM. Habitual coffee consumption and risk of type 2 diabetes, ischemic heart disease, depression and Alzheimer's disease: a Mendelian randomization study. Sci Rep. 2016;6:36500. doi:10.1038/srep36500
Kim J, Aschard H, Kang JH, et al. Intraocular Pressure, Glaucoma, and Dietary Caffeine Consumption: A Gene-Diet Interaction Study from the UK Biobank. Ophthalmology. 2021;128(6):866–76. doi:10.1016/j.ophtha.2020.12.009
Terai N, Spoerl E, Pillunat LE, Stodtmeister R. The effect of caffeine on retinal vessel diameter in young healthy subjects. Acta Ophthalmol. 2012;90(7):e524-8. doi:10.1111/j.1755-3768.2012.02486.x
Sanni O, Terre'Blanche G. Therapeutic potentials of agonist and antagonist of adenosine receptors in type 2 diabetes. Rev Endocr Metab Disord. 2021;22(4):1073–90. doi:10.1007/s11154-021-09668-8
Fink JC. Chronic kidney disease: the effect of CKD therapies on serum potassium levels. Nat Rev Nephrol. 2010;6(11):633–4. doi:10.1038/nrneph.2010.127

No competing interests reported.

Download PDF

Reviews received at journal
28 Jul, 2024
Reviewers agreed at journal
19 Jul, 2024
Reviewers invited by journal
19 Jul, 2024
Editor assigned by journal
19 Jul, 2024
Editor invited by journal
16 Jul, 2024
Submission checks completed at journal
12 Jul, 2024
First submitted to journal
10 Jul, 2024

You are reading this latest preprint version

Coffee intake and risk of diabetic retinopathy: a Mendelian randomization study

Status:

Version 1

Abstract

Figures

INTRODUCTION

METHODS

Study design description

Selection of instrumental variables for MR analyses

Statistical analysis

RESULTS

The casual effect of coffee intake on DR

Finn-b-DM_RETINOPATHY

Finn-b-H7_RETINOPATHYDIAB

Finn-b-DM_RETINOPATHY_EXMORE

Finn-b-DM_RETINA_PROLIF

Finn-b-H7_RETINOPATHYDIAB_PROLIF

Finn-b-H7_RETINOPATHYDIAB_BKG

DISCUSSION

CONCLUSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1