Genetically proxied therapeutic inhibition of kidney function drug targets and type 2 diabetes in Africans: A Mendelian randomization study

Genetically proxied therapeutic inhibition of kidney function drug targets and type 2 diabetes in Africans: A Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetically proxied therapeutic inhibition of kidney function drug targets and type 2 diabetes in Africans: A Mendelian randomization study Abdoulaye Diawara, Mariam Traore, Oudou Diabaté, Christopher Kintu, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3956597/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Despite the growing number of treatments available for diabetics, many people fail to achieve their therapeutic goals. The complexity of diabetes prevention and control exacerbates the situation in low-income countries. These complexities include genetic factors, social, and financial burdens. Strategies for optimizing coverage for new drugs and clinical therapies for type 2 diabetes mellitus (T2DM) have focused on dual-use approaches for new or off-label indications. This study aimed to determine whether inhibition of kidney function drug targets have adverse effect on T2DM. Methods: A two-sample Mendelian randomization (MR) study was conducted based on genetic variants located in or near genes (in 300 kilobyte windows) for encoding significant drug targets. We used summary statistics of eGFR GWAS (n=80,027) of African ancestry individuals and GWAS datasets of T2DM (n=4,347 Africans in South Africa, Nigeria, Ghana and Kenya), to predict the effects of drug exposure on T2DM risk. Results: Genetically predicted inhibition of vascular endothelial growth factor A (VEGFA) and Ras homolog enriched in brain (RHEB) were associated with higher odds of T2DM incidence (OR, 2.66; 95% CI 1.34–3.78, and OR, 2.25; 95% CI, 1.34–3.28, respectively). Genetically predicted inhibition of SLC22A2 and inhibition of CLDN14 were not associated with T2DM occurrence (OR, 0.95; 95% CI, 0.61-1.48 and OR, 1.56; 0.71–2.20, respectively). Interpretation : Our results suggest VEGFA inhibitors and RHEB inhibitors drugs may increase the risk or exacerbate T2DM risk in Africans, hence a need for closely monitoring the safety and efficacy of anti-diabetic drugs in the African population. Diabetes drug repurposing kidney disease estimated glomerular filtration rate Mendelian randomization Africa Figures Figure 1 Figure 2 Introduction The International Diabetes Federation (IDF) has reported that about 537 million adults (aged 20–79 years) had diabetes in 2021, which accounts for 10.5% of the adult population in this age group( 1 , 2 , 3 ). Diabetes represents a significant global health problem, and its prevalence rates are expected to accelerate ( 1 , 4 ). Drug repositioning (commonly known as repurposing) is the use of a drug in an indication other than that for which it was originally formulated ( 15 ). There is no treatment to prevent or cure type 2 diabetes (T2DM). Extending the indications of drugs with established efficacy to other new indications is a powerful approach for new drugs and clinical treatments for T2DM. The complexity of diabetes prevention and control is exacerbated in low-income countries, including those in Africa, due to genetic factors and logistical, social, and financial burdens( 12 , 13 , 14 , 15 , 14 ). The prevalence of diabetes is higher in low- and middle-income countries, and despite available therapeutic options, many patients fail to achieve optimal control (5, 6,. Access to anti-diabetic drugs can be more challenging in low-income countries ( 12 ). Therefore, repurposing drugs for type 2 diabetes in African populations could be a promising strategy to improve treatment outcomes, however this could elicit some adverse effect. Diabetes is one of the main risk causes for kidney disease. Approximately one in three adult diabetics suffer from chronic kidney disease (CKD), and every 24 hours, 170 diabetics start taking kidney failure medication ( 16 , 17 ). The kidneys are a key organ in regulating bodily liquid volume, maintaining electrolyte equilibrium, and the elimination/ re-uptake of internal and external compounds ( 15 ). Within these renal functions, the ability to excrete/reabsorb both endogenous and exogenous chemicals is vital for ensuring physiological homeostasis in the organism ( 16 ). The estimated glomerular filtration rate (eGFR) measures healthy kidney function (the amount of blood the kidneys filter per minute, based on body size). The eGFR test is an essential diagnostic tool for detecting kidney disease at an early stage, monitoring kidney function, identifying risk factors, assessing drug dosages, and developing personalized treatment plans ( 17 ). Diabetes and kidney disease are very closely linked, with several classes of drugs that target both disorders. Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a class of drugs for the treatment of type 2 diabetes that block the renal reabsorption of blood glucose, thereby improving the excretion of sugar in the urine( 18 , 21 ). SGLT2 inhibitors have been revealed to have protecting effects on the kidneys of patients with type 2 diabetes and diabetic nephropathy, reducing the risk of renal failure and cardiovascular events( 19 ). Platinum-based drugs, like cisplatin and oxaliplatin, are largely explored in the organ cation transporter arena, including SLC22A1 and SLC22A2( 20 ). Organic anion transporters (OAT or SLC22A) play an extremely vital role in renal dysfunction and are widely recognized as drug transporters. Several distinct isoforms belong to the SLC22A family ( 21 ). These isoforms present different transport substance profiles and renal localization. Previous studies have shown that genetic variations in the SLC22A1, SLC22A2, and SLC22A3 genes have been found to affect glycemic control and response to metformin in patients with type 2 diabetes ( 25 , 26 , 27 , 28 ). Mendelian randomization (MR) applies genetic variants to judge the causal relationship between a risk factor and an outcome based on observational data. In one phase at a time ( 26 ). MR studies are less subject to confounding, bias, and reverse causation than observational designs, as genetic variants are attributed to the chance of conception and are not mediated by environmental or lifestyle factors ( 29 , 30 ). MR can provide a feasible alternative approach to assessing a drug's effect ( 31 , 32 ). In short, MR applies genetic variations, generally single nucleotide polymorphisms (SNPs), as exposure tools to give the ideal, unfounded influence of exposure on outcome. Genetic alternatives found at the genetic locus of the gene encoding the medication's affect protein occur thought to impact the effect of exposure on outcome. The drug's target protein is believed to impact protein expression or function( 30 ). Exploiting these variations as genetic tools can imitate in what manner the medication controls its target protein, helping us predict the effect of exposure on outcome. In this way, we can evaluate the effect of a genetic variation in the drug's target on a new indication, as in a RCT. ( 31 ) This study sought to assess the effect of renal dysfunction drugs in type 2 diabetes mellitus using MR approaches with genetic variants in positions of genes coding targets of different renal drug targets in African populations. Methods Study Design Mendelian randomization (MR) is a powerful study design that uses genetic variants as instrumental variables to assess causal relationships between risk factors and outcomes. In this study, genetic variants located in genes encoding relevant drug targets were used as instruments ( 36 ). These genetic variants were selected based on their associations with estimated glomerular filtration rate (eGFR) and T2DM risk from large-scale genome-wide association studies (GWAS) in African populations(Fig. 1 ). By leveraging these genetic instruments, the study aimed to predict the effects of drug use on the development of type 2 diabetes mellitus (T2DM) in African populations, providing valuable insights into the potential for repurposing renal dysfunction drugs for T2DM prevention and treatment. Data sources We collected genetic association estimates by analyzing data from large size genome association studies (GWAS). To select our instruments, we relied on association estimates from analysis of GWAS focused on eGFR in people of African descent. This meta-analysis included three groups: the Million Veteran Program (MVP) with 57,336 individuals of African ancestry, the Chronic Kidney Disease Genetics Consortium with 16,474 individuals of African descent and the UK Biobank with 16,474 individuals of African ancestry. To uncover genetic clues to type 2 diabetes in Africans, we used summary statistics from two different cohorts: South Africa's Zulu population: This is a cohort from the Durban Diabetes Study (DDS) and the Durban Diabetes Case Control Study (DCC), both focused on Zulu adults residing in Durban( 32 ). This includes samples from 2578 individuals (1602 cases and 976 controls) genotyped on Illumina's multi-ethnic genotyping network and imputed to a combined panel incorporating datasets from 1000 genomes and Africa America Diabetes Mellitus (AADM) study: Recruited from academic medical centers in Nigeria, Ghana and Kenya, this cohort included people with ADA (American Diabetes Association) criteria - or treatment-confirmed type 2 diabetes. After quality control, they analyzed data from 1769 participants (1031 cases and 738 controls) genotyped with the Affymetrix Axiom PANAFR SNP array and imputed using the Burrows-Wheeler positional transformation method versus the Sanger imputation server. Genetic instruments for kidney disease drugs We examined vascular Endothelial Growth Factor (VEGF) inhibitor drug class, encoded by the VEGFA gene ( 42 , 43 ). CLDN14 gene encodes a tight junction protein a class of drugs that target the tight junctions between cells( 35 ).RHEB (Ras homologous enriched in the brain) a protein GTPase implicated in the inhibition of cell growth and inflammation RHEB is it’s encoding gene.SLC22A2 is a gene encoding the OCT2 protein( 36 )(Table 1 ). These genes were selected as drug targets based on their known roles in pathways related to endothelial dysfunction, serum creatinine, fibrosis, inflammation, eGFR and decline in renal function, making them potential candidates for drug tailoring efforts in the context of type 2 diabetes and associated renal complications. We selected single nucleotide polymorphisms (SNPs) located within or nearby each coding gene (selected previously) and associated with eGFR at a genome-wide significance level (P < 5×10 − 8) (S1). We performed clumping to identify independent SNPs using the linkage disequilibrium between them. Clumping was used to prevent SNPs from being correlated with each other in our instruments. The idea is to retain the most representative SNPs for each region of linkage disequilibrium( 37 ) because, when correlated, SNPs can influence each other's causal pathway. To ensure that the effect of a SNP on exposure and outcome corresponded to the same allele, we harmonized the data and excluded palindromic SNPs whose minor allele frequency was > 40% ( 38 ). The F-statistic was determined for the different variants used as instruments to measure instrument strength based on the way earlier report by Burgess et al( 39 ). Table 1 Information about the drug targets and encoding genes. Drug target NSNPs Encoding Genes e.g., Location RHEB inhibitors 9 RHEB Rapamycin Chr7: 151,163,098–151,217,206 CLDN14 modulators 4 CLDN14 Berberine Chr21 : 37,832,919 − 37,948,867 VEGFA inhibitors 6 VEGFA Avastin Chr6 : 43,737,921 − 43,754,224 SLC22A2 inhibitors 8 SLC22A2 Cimetidine Chr 6: 160,592,093–160,698,670 Statistical methods In this two-sample MR analysis, the weighted inverse variance (WIV) approach was applied in the R package for Mendelian randomisation as the main analysis method, as it is the most suitable method to use with summarised data.( 48 , 29 ). Sensitivity analyses We used the MR-Egger, weighted median, and weighted mode methods as sensitivity analyses, since these approaches are the most robust to the plausible pleiotropic effects of the variants used as instrumental variables( 41 ). There are no horizontal pleiotropic effects when the MR-Egger intercept test is not statistically significant. If it is significant, it indicates that the IVW may be biased( 42 ). Although directionally consistent results from different methods may strengthen our conclusions, with MR showing evidence of P < 0.05, colocalization analysis was used to test whether exposure-associated and target-drug-associated genetic variants were in the same genomic region and shared a common causal variant. On a Bayesian basis, we set the a priori probability of association between all the variants and one or other trait at 1 × 10 − 4 and the a priori probability of a causal variant shared between two traits at 1 × 10 − 5. ("coloc" R package)( 52 , 29 ). Results Genetic instruments for kidney disease drug targets Table 2 presents a synthesis of the instruments used for each exposure. F-statistics for all single variants range from 30.085 to 243.297, indicating a minimal risk of instrumenting and low bias (Table 2 ). Table 2 Instrumental variables for the exposure. Exposures NSNPs Median F (range) RHEB inhibitors 9 136.84 (38.08-243.29) CLDN14 modulators 4 34.02 (30.08–37.03) VEGFA inhibitors 6 91.98 (35.1-96.11) SLC22A2 inhibitors 8 141.10 (41.22-127.59) Mendelian randomization analysis We identified strong support that both genetically predicted inhibition of VEGFA and inhibition of RHEB were associated with an increased incidence of T2DM (OR 2.66, 95% CI = 1.34–3.78, P = 0.001) and (OR 2.25, 95% CI = 1.34–3.28, P = 0.001) respectively (Fig. 2 ). there was no evidence of an association between genetically predicted inhibition of SLC22A2 or inhibition of CLDN14 and the risk of T2DM (OR [95% CI] 0.95 [1.56–1.48], P = 0.84) (OR [95% CI] 0.95 [0.71–2.20], P = 0.84) respectively (Table 3 ), (Fig. 2 ). Table 3 Univariable IVW Mendelian Randomization results. Exposure Outcome BETA SE 95% CI p-value RHEB inhibitors T2DM 0.815 0.815 1,347 to 3,289 0,001 CLDN14 modulators T2DM 0.940 0.940 0.712 to 2.207 0.149 VEGFA inhibitors T2DM 0.981 0.981 1.843 to 3.335 0.001 SLC22A2 inhibitors T2DM −0.044 -3.007 0.615 to 1.483 0.841 RHEB inhibitors, CLDN14 modulators; VEGFA inhibitors; SLC22A2 inhibitors; IVW, Inverse Variance Weighted; SE, standard error. Sensitivity analyses According to the MR-Egger method, there was no significant evidence of heterogeneity between the genetic variants used as instrumental variables in the MR analysis (Q_pva = 0.917); nor of significant evidence of directional pleiotropy with an intercept egger of 0.0799, p-value = 0.194, indicating robust results in the sensitivity analyses. Colocalization analysis didn't show enough support for shared causal variant of genetically predicted inhibition of RHEB and VEGFA with our outcome data (PPshared = 6.49%, and PPshared = 11.02) respectively. Discussion In this study, we performed T2MR analyses to investigate associations between kidney disease drugs target and the risk of type 2 diabetes in the African population. We examined Vascular Endothelial Growth Factor (VEGF) inhibitor drug class. VEGFA is a protein that is important for the growth and development of blood vessels. VEGF/VEGFR inhibitors [for example, Bevacizumab (Avastin), Pazopanib (Votrient)] are a class of drugs that inhibit the action of vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor (VEGFR) ( 53 , 54 ) used to treat various types of cancer, including kidney cancer ( 46 ), VEGF inhibitors act by inhibiting angiogenesis through inhibition of the VEGF pathway. VEGF inhibitors belong to a group of drugs known as "antiangiogenic agents", working by preventing the growth of blood vessels that supply tumors with oxygen and nutrients( 47 ). The results show a negative effect of Vascular Endothelial Growth Factor (VEGF) inhibitor drug class on type 2 diabetes risk. Our finding demonstrates that mTOR inhibitors like RHEB can be harmful for type 2 diabetic patients and increase its occurrence. RHEB is a protein GTPase implicated in the inhibition of cell growth and inflammation via its involvement in the mTORC1 (mechanistic target of rapamycin complex 1) signaling pathway ( 48 ). Rapamycin, an mTOR inhibitor, reduces excessive interstitial inflammation, fibrosis and renal performance decline that occur at the onset of chronic kidney disease (CKD)( 49 ) ( Fig. 1 ). We also observed that the Claudin 14 (CLDN14) and SLC22A2 have no significant effect on diabetes risk. CLDN14 gene encodes a tight junction protein that plays a crucial role in preserving the glomerular filtration barrier in the kidney. The mechanism of the drug class targeting CLDN14 is that of tight junction modulators. Tight junction modulators (Claudin-1-specific peptide AT1002, Berberine) are a class of drugs that target the tight junctions between cells, which are responsible for maintaining the integrity of the epithelial barrier ( 50 ). These drugs can be used to treat of various conditions, including kidney disease, by improving tight junction function and decreasing the permeability of the intracellular epithelial barrier membrane( 51 ). SLC22A2 encoding the OCT2 protein, is known for its important role in the renal elimination of cationic drugs and endogenous organic cations ( 52 ),( 53 ). The OCT2 protein is a polyspecific transporter that facilitates the absorption of a wide range of cationic drugs, including several used to treat kidney disease, such as cimetidine, famotidine, oxycodone, procainamide, and ranitidine( 54 ). One study showed that SLC22A2 is associated with tubular creatinine secretion and distorted estimated glomerular filtration rate (eGFR) in kidney transplantation, ( 55 ). Our results revealed evidence linking genetically predicted inhibition of vascular endothelial growth factor A (VEGFA) and brain-enriched Ras homolog (RHEB) to higher incidences of type 2 diabetes (T2DM) in Africans, whereas genetically predicted inhibition of SLC22A2 or inhibition of CLDN14 showed no association with T2DM risk. It should therefore be noted that drugs used to treat renal dysfunction can have a detrimental effect on type 2 diabetes and adversely affect the patient's health. Our findings may be particularly significant for pre-diabetics and diabetics with renal dysfunction, for whom the preferential use of VEGFA and RHEB inhibitors for their treatment may have an additive impact on, or possibly worsen, the risk of developing diabetes compared to other therapies for renal dysfunction. Mendelian randomization studies rely on the assumption that genetic variants are not influenced by confounding factors, which may not always hold true (69,70,71,72). Additionally, the study focused on genetic variants in African populations but did not explore potential differences across subpopulations or consider other factors that may influence drug response(74, 75). Further research is needed to validate the findings of this study and explore additional genetic and non-genetic factors that may impact the effectiveness of repurposed drugs for type 2 diabetes in African populations. Conclusion In this study, we add to the existing breadth of research on the potential role of kidney disease medications in preventing type 2 diabetes mellitus (T2DM) through Mendelian randomisation. By incorporating the effects of genetic variants that have a significant impact on estimated glomerular filtration rate (eGFR) in individuals of African descent, alongside targeted drug therapies for kidney disorders, we uncover compelling evidence suggestive of a possible link between specific drug classes and an elevated risk for T2DM. Abbreviations ACE: Angiotensin-converting enzyme ADA: American Diabetes Association ARB: Angiotensin II receptor blocker BMI: Body mass index CKD: Chronic kidney disease OAT: Organic anion transporters. eGFR: Estimated glomerular filtration rate. HbA1c: Glycated haemoglobin SGLT2: Sodium-glucose cotransporter 2 GFR: Glomerular filtration rate GWAS: Genome-wide association study MR: Mendelian randomization IDF: International Diabetes Federation RCT: Randomized controlled trial. SLC22A1: Solute-like carrier family 22 member 1. SLC22A: Solute-like carrier family 22 members. SLC22A2: Solute-like carrier family 22 member 2. SLC22A3: Solute-like carrier family 22 member 3 CKDGen: Chronic Kidney Disease genetics. VEGFA: vascular endothelial growth factor A RHEB: Ras homolog enriched in brain. T2DM: Type 2 diabetes mellitus. SNP: Single nucleotide polymorphism. UK Biobank (UKBB). MVP: Million Veteran Program. AADM: The Africa America Diabetes Mellitus (AADM) IVW: Inverse variance weighted. VEGFR: vascular endothelial growth factor receptor mTOR: mammalian target of rapamycin Declarations Consent for publication: Not Applicable. Funding: OS is supported by the African Research Excellence Fund (AREF-325-SORE-F-C0904). SF is supported by Wellcome Trust grant 220740/Z/20/Z. AD is supported by a grant from the Fogarty International Centre and the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under number U2RTW010673 for the West African Centre of Excellence for Bioinformatics Research Training in Global Health through the African Centre of Excellence in Bioinformatics in Bamako. Acknowledgments: The authors thank Million Veteran Program (MVP) staff, researchers, and volunteers, who have contributed to MVP, and especially participants who previously served their country in the military and now generously agreed to enroll in the study. (See https://www.research.va.gov/mvp/ for more details). The citation for MVP is Gaziano, J.M. et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J Clin Epidemiol 70, 214-23 (2016). This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by the Veterans Administration (VA) Cooperative Studies Program (CSP) award #G002. “Data was accessed through approved dbGaP proposal #30287 entitled, “Genomic determinant of Complex Diseases in African ancestry individuals”. Author contributions: SF and OS conceptualized the study. AD carried out the analyses. OS, MT and SF checked the underlying data. AD, OS, MT, and SF drafted the first version of the manuscript. SF, CK, MT and OS interpreted the results. KT, OD, ON, SOD, TYA, JGS and MD interpreted the data. AAS, JOA, OS, MT, OD, CC, MW, ON, SOD, JGS, TYA, MD, SF, made critical revisions and modifications to the manuscript. All authors have read and approved the final manuscript. SF is the guarantor of this work. All authors have read and approved the final version of the manuscript. Availability of data and materials: The datasets used in this study were obtained from the following sources: MVP dataset: This dataset was accessed through the dbGaP database under proposal number #30287.UKBB eGFRcrea summary statistics: This dataset was downloaded from the UK Biobank using phenocode 30700. CKDGen dataset: This dataset was downloaded from the CKDGen consortium website. (https://ckdgen.imbi.uni-freiburg.de/datasets/Gorski_2021 ). T2DM GWAS summary statistics: available to download from the EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/31049640 ) All scripts are available on request from corresponding authors. Conflicts of interest: The co-authors declared no potential conflict of interest in this article. Ethical consideration No ethical approval is required for this research as the data used are summary data are publicly available unless otherwise stated. References Ogurtsova K, Guariguata L, Barengo NC, Ruiz PLD, Sacre JW, Karuranga S, et al. 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Lin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLOS Genet. 2021;17(11):e1009922. Burgess S, Foley CN, Allara E, Staley JR, Howson JMM. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun. 2020;11(1):376. Rasooly D, Peloso GM, Giambartolomei C. Bayesian Genetic Colocalization Test of Two Traits Using coloc. Curr Protoc. 2022;2(12):e627. Vascular Endothelial Growth Factor Inhibitors. | DrugBank Online [Internet]. [cited 2023 Nov 29]. Available from: https://go.drugbank.com/categories/DBCAT004320 . Sharma PS, Sharma R, Tyagi T. VEGF/VEGFR pathway inhibitors as anti-angiogenic agents: present and future. Curr Cancer Drug Targets. 2011;11(5):624–53. Cardones AR, Banez LL. VEGF inhibitors in cancer therapy. Curr Pharm Des. 2006;12(3):387–94. Sharma PS, Sharma R, Tyagi T. VEGF/VEGFR pathway inhibitors as anti-angiogenic agents: present and future. Curr Cancer Drug Targets. 2011;11(5):624–53. Jiang L, Xu L, Mao J, Li J, Fang L, Zhou Y, et al. Rheb/mTORC1 signaling promotes kidney fibroblast activation and fibrosis. J Am Soc Nephrol JASN. 2013;24(7):1114–26. Gui Y, Dai C, mTOR. Signal Kidney Dis Kidney360. 2020;1(11):1319–27. Staat C, Coisne C, Dabrowski S, Stamatovic SM, Andjelkovic AV, Wolburg H, et al. Mode of action of claudin peptidomimetics in the transient opening of cellular tight junction barriers. Biomaterials. 2015;54:9–20. Prot-Bertoye C, Houillier P. Claudins in Renal Physiology and Pathology. Genes. 2020;11(3):290. Wilson NC, Choudhury A, Carstens N, Mavri-Damelin D. Organic Cation Transporter 2 (OCT2/SLC22A2) Gene Variation in the South African Bantu-Speaking Population and Functional Promoter Variants. OMICS J Integr Biol. 2017;21(3):169–76. SLC22A2 - an. overview | ScienceDirect Topics [Internet]. [cited 2023 Nov 29]. Available from: https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/slc22a2 . Karbach U, Kricke J, Meyer-Wentrup F, Gorboulev V, Volk C, Loffing-Cueni D, et al. Localization of organic cation transporters OCT1 and OCT2 in rat kidney. Am J Physiol-Ren Physiol. 2000;279(4):F679–87. Swerdlow DI. Mendelian Randomization and Type 2 Diabetes. Cardiovasc Drugs Ther. 2016;30:51–7. Davies NM, Holmes MV, Smith GD. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. Lee K, Lim CY. Mendelian Randomization Analysis in Observational Epidemiology. J Lipid Atheroscler. 2019;8(2):67–77. Gill D, Georgakis MK, Walker VM, Schmidt AF, Gkatzionis A, Freitag DF, et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res. 2021;6:16. Smith GD, Ebrahim S. Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies. In: Biosocial Surveys [Internet]. National Academies Press (US); 2008 [cited 2023 Nov 29]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK62433/ . Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3956597","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275507175,"identity":"c36c75c4-0eaf-4e04-87ca-de1773823f4f","order_by":0,"name":"Abdoulaye Diawara","email":"","orcid":"","institution":"University of Sciences, Techniques and Technologies of Bamako","correspondingAuthor":false,"prefix":"","firstName":"Abdoulaye","middleName":"","lastName":"Diawara","suffix":""},{"id":275507176,"identity":"0f932ee5-60ca-4260-9775-6b8b5c40ccda","order_by":1,"name":"Mariam Traore","email":"","orcid":"","institution":"University of Sciences, Techniques and Technologies of Bamako","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"","lastName":"Traore","suffix":""},{"id":275507178,"identity":"112a0fd1-a57e-4df7-a2c1-b1b49625a488","order_by":2,"name":"Oudou Diabaté","email":"","orcid":"","institution":"University of Sciences, Techniques and Technologies of Bamako","correspondingAuthor":false,"prefix":"","firstName":"Oudou","middleName":"","lastName":"Diabaté","suffix":""},{"id":275507180,"identity":"acfe42fa-92df-4c69-b2b3-272cca51a6ea","order_by":3,"name":"Christopher Kintu","email":"","orcid":"","institution":"The African Computational Genomics (TACG) Research group","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Kintu","suffix":""},{"id":275507181,"identity":"65a4ea5f-fb83-478c-8742-aa7d7999a235","order_by":4,"name":"Ali Awadallah Saeed","email":"","orcid":"","institution":"Faculty of Pharmacy, National University-Sudan, Mycetoma Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Awadallah","lastName":"Saeed","suffix":""},{"id":275507183,"identity":"01ceaf55-ee6f-4feb-aacb-a31f76eafcef","order_by":5,"name":"Julianah Ore Abiola","email":"","orcid":"","institution":"Center for Genomics Research and Innovation","correspondingAuthor":false,"prefix":"","firstName":"Julianah","middleName":"Ore","lastName":"Abiola","suffix":""},{"id":275507184,"identity":"f3848842-ebf0-4c16-9e59-1b0c010e0a3d","order_by":6,"name":"Cheickna Cisse","email":"","orcid":"","institution":"University of Sciences, Techniques and Technologies of Bamako","correspondingAuthor":false,"prefix":"","firstName":"Cheickna","middleName":"","lastName":"Cisse","suffix":""},{"id":275507186,"identity":"bb29d185-97e7-424d-ba03-9a0b90bd66ed","order_by":7,"name":"Kassim Traore","email":"","orcid":"","institution":"Duquesne University","correspondingAuthor":false,"prefix":"","firstName":"Kassim","middleName":"","lastName":"Traore","suffix":""},{"id":275507188,"identity":"f40309eb-9f18-4621-9c5c-4eb554c83adc","order_by":8,"name":"Mamadou Wele","email":"","orcid":"","institution":"University of Sciences, Techniques and Technologies of Bamako","correspondingAuthor":false,"prefix":"","firstName":"Mamadou","middleName":"","lastName":"Wele","suffix":""},{"id":275507190,"identity":"12851b0f-c857-4486-bd52-1f125d3011b0","order_by":9,"name":"Oyekanmi Nash","email":"","orcid":"","institution":"Center for Genomics Research and Innovation","correspondingAuthor":false,"prefix":"","firstName":"Oyekanmi","middleName":"","lastName":"Nash","suffix":""},{"id":275507193,"identity":"c5dddb51-691c-48c7-89fc-9f7edc2038c4","order_by":10,"name":"Seydou O. Doumbia","email":"","orcid":"","institution":"University of Sciences, Techniques and Technologies of Bamako","correspondingAuthor":false,"prefix":"","firstName":"Seydou","middleName":"O.","lastName":"Doumbia","suffix":""},{"id":275507194,"identity":"510c30b8-aa64-4482-a172-b814ffaf6df9","order_by":11,"name":"Talib Yusuf Abbas","email":"","orcid":"","institution":"Burhani College","correspondingAuthor":false,"prefix":"","firstName":"Talib","middleName":"Yusuf","lastName":"Abbas","suffix":""},{"id":275507195,"identity":"21904e77-b1bd-4707-84df-ac7b3d50b06c","order_by":12,"name":"Jeffrey G. Shaffer","email":"","orcid":"","institution":"Tulane University School of Public Health and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"G.","lastName":"Shaffer","suffix":""},{"id":275507196,"identity":"05d1540d-e54b-4519-8dc1-452a567f3acc","order_by":13,"name":"Mahamadou Diakité","email":"","orcid":"","institution":"University of Sciences, Techniques and Technologies of Bamako","correspondingAuthor":false,"prefix":"","firstName":"Mahamadou","middleName":"","lastName":"Diakité","suffix":""},{"id":275507197,"identity":"55458320-1985-4827-91af-262fbef9311e","order_by":14,"name":"Segun Fatumo","email":"","orcid":"","institution":"Medical Research Council, Uganda Virus Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Segun","middleName":"","lastName":"Fatumo","suffix":""},{"id":275507198,"identity":"73394120-fb4c-4212-b548-678284a9ed7a","order_by":15,"name":"Opeyemi Soremekun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYPACCSh9gIGBH0QnFJCiRbIBpMWAaNuAWgwOgBh4tPBLNx+T+JhjYc8vffjohg9nDucZn1+d+OGBAYM8v9gBrFok5xxLk5y5TSJxZl9a2s0ZNw4Xm914u1kC6DDDmbMTsGoxuJFjJs27DajmDI/ZbZ4PhxO33Ti7AaQlweA2Li3536T/bpOwNzjD/+32H6CWzTPObv6BX0sOmzTjNgnGDWd42G4z3DicuIG/dxteW4B+MbbsBfmlh83sZs+Z9MQZN3i3WSQYSOD0CzDEHt74ua3Onp+H+dmNH8esE/v7z26++aPCRp5fGrsWYCSySKCJJEDEcQIJBuYPaBYfwK16FIyCUTAKRiQAABkpaIGZBJpqAAAAAElFTkSuQmCC","orcid":"","institution":"The African Computational Genomics (TACG) Research group","correspondingAuthor":true,"prefix":"","firstName":"Opeyemi","middleName":"","lastName":"Soremekun","suffix":""}],"badges":[],"createdAt":"2024-02-14 16:30:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3956597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3956597/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52025767,"identity":"b6d4db3b-fa16-49da-b59f-2fb05c7df688","added_by":"auto","created_at":"2024-03-05 15:49:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDiagram of drug target genes (VEGF: Vascular endothelial growth factor, SLC22A2: Solute carrier family 22 member A2, CLDN14: Claudin-14. RHEB: Ras homolog enriched in brain.by drugs) and variants affecting kidney function through endothelial dysfunction, serum creatinine, fibrosis, inflammation, and eGFR.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage166.png","url":"https://assets-eu.researchsquare.com/files/rs-3956597/v1/2dcdb53e7e1287366a12c2cb.png"},{"id":52025769,"identity":"abe5a078-a6e0-42e6-ae9b-c830408b598b","added_by":"auto","created_at":"2024-03-05 15:49:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304937,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the odds ratio and their 95% confidence intervals relating genetically predicted kidney disease drug targets and eGFR using the IVW univariable MR method, Weighted Median, MR-Egger. IVW, inverse-variance weighted; RHEB, Ras homolog enriched in brain; CLDN14, claudin 14; VEGFA, vascular endothelial growth factor A; SLC22A2, solute carrier family 22.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3956597/v1/001520bf8422b0c2ba5f6104.jpeg"},{"id":56513790,"identity":"ed7b5287-515f-4721-9e50-cd3856ee72e5","added_by":"auto","created_at":"2024-05-15 07:14:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":901278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3956597/v1/f6bb733b-c2c8-4f3a-b199-e8645943afb0.pdf"},{"id":52025768,"identity":"4872740b-437d-4a83-aa9c-b15a27996ffb","added_by":"auto","created_at":"2024-03-05 15:49:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17702,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-3956597/v1/9e279231753eae31eece2e40.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetically proxied therapeutic inhibition of kidney function drug targets and type 2 diabetes in Africans: A Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe International Diabetes Federation (IDF) has reported that about 537\u0026nbsp;million adults (aged 20\u0026ndash;79 years) had diabetes in 2021, which accounts for 10.5% of the adult population in this age group(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Diabetes represents a significant global health problem, and its prevalence rates are expected to accelerate (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Drug repositioning (commonly known as repurposing) is the use of a drug in an indication other than that for which it was originally formulated (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). There is no treatment to prevent or cure type 2 diabetes (T2DM). Extending the indications of drugs with established efficacy to other new indications is a powerful approach for new drugs and clinical treatments for T2DM.\u003c/p\u003e \u003cp\u003eThe complexity of diabetes prevention and control is exacerbated in low-income countries, including those in Africa, due to genetic factors and logistical, social, and financial burdens(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The prevalence of diabetes is higher in low- and middle-income countries, and despite available therapeutic options, many patients fail to achieve optimal control (5, 6,. Access to anti-diabetic drugs can be more challenging in low-income countries (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, repurposing drugs for type 2 diabetes in African populations could be a promising strategy to improve treatment outcomes, however this could elicit some adverse effect.\u003c/p\u003e \u003cp\u003eDiabetes is one of the main risk causes for kidney disease. Approximately one in three adult diabetics suffer from chronic kidney disease (CKD), and every 24 hours, 170 diabetics start taking kidney failure medication (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe kidneys are a key organ in regulating bodily liquid volume, maintaining electrolyte equilibrium, and the elimination/ re-uptake of internal and external compounds (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Within these renal functions, the ability to excrete/reabsorb both endogenous and exogenous chemicals is vital for ensuring physiological homeostasis in the organism (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The estimated glomerular filtration rate (eGFR) measures healthy kidney function (the amount of blood the kidneys filter per minute, based on body size). The eGFR test is an essential diagnostic tool for detecting kidney disease at an early stage, monitoring kidney function, identifying risk factors, assessing drug dosages, and developing personalized treatment plans (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Diabetes and kidney disease are very closely linked, with several classes of drugs that target both disorders.\u003c/p\u003e \u003cp\u003eSodium-glucose cotransporter 2 (SGLT2) inhibitors are a class of drugs for the treatment of type 2 diabetes that block the renal reabsorption of blood glucose, thereby improving the excretion of sugar in the urine(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). SGLT2 inhibitors have been revealed to have protecting effects on the kidneys of patients with type 2 diabetes and diabetic nephropathy, reducing the risk of renal failure and cardiovascular events(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlatinum-based drugs, like cisplatin and oxaliplatin, are largely explored in the organ cation transporter arena, including SLC22A1 and SLC22A2(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Organic anion transporters (OAT or SLC22A) play an extremely vital role in renal dysfunction and are widely recognized as drug transporters. Several distinct isoforms belong to the SLC22A family (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These isoforms present different transport substance profiles and renal localization. Previous studies have shown that genetic variations in the SLC22A1, SLC22A2, and SLC22A3 genes have been found to affect glycemic control and response to metformin in patients with type 2 diabetes (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) applies genetic variants to judge the causal relationship between a risk factor and an outcome based on observational data. In one phase at a time (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). MR studies are less subject to confounding, bias, and reverse causation than observational designs, as genetic variants are attributed to the chance of conception and are not mediated by environmental or lifestyle factors (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). MR can provide a feasible alternative approach to assessing a drug's effect (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In short, MR applies genetic variations, generally single nucleotide polymorphisms (SNPs), as exposure tools to give the ideal, unfounded influence of exposure on outcome. Genetic alternatives found at the genetic locus of the gene encoding the medication's affect protein occur thought to impact the effect of exposure on outcome. The drug's target protein is believed to impact protein expression or function(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Exploiting these variations as genetic tools can imitate in what manner the medication controls its target protein, helping us predict the effect of exposure on outcome. In this way, we can evaluate the effect of a genetic variation in the drug's target on a new indication, as in a RCT. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis study sought to assess the effect of renal dysfunction drugs in type 2 diabetes mellitus using MR approaches with genetic variants in positions of genes coding targets of different renal drug targets in African populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a powerful study design that uses genetic variants as instrumental variables to assess causal relationships between risk factors and outcomes. In this study, genetic variants located in genes encoding relevant drug targets were used as instruments (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These genetic variants were selected based on their associations with estimated glomerular filtration rate (eGFR) and T2DM risk from large-scale genome-wide association studies (GWAS) in African populations(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By leveraging these genetic instruments, the study aimed to predict the effects of drug use on the development of type 2 diabetes mellitus (T2DM) in African populations, providing valuable insights into the potential for repurposing renal dysfunction drugs for T2DM prevention and treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData sources\u003c/p\u003e \u003cp\u003eWe collected genetic association estimates by analyzing data from large size genome association studies (GWAS). To select our instruments, we relied on association estimates from analysis of GWAS focused on eGFR in people of African descent. This meta-analysis included three groups: the Million Veteran Program (MVP) with 57,336 individuals of African ancestry, the Chronic Kidney Disease Genetics Consortium with 16,474 individuals of African descent and the UK Biobank with 16,474 individuals of African ancestry.\u003c/p\u003e \u003cp\u003eTo uncover genetic clues to type 2 diabetes in Africans, we used summary statistics from two different cohorts: South Africa's Zulu population: This is a cohort from the Durban Diabetes Study (DDS) and the Durban Diabetes Case Control Study (DCC), both focused on Zulu adults residing in Durban(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This includes samples from 2578 individuals (1602 cases and 976 controls) genotyped on Illumina's multi-ethnic genotyping network and imputed to a combined panel incorporating datasets from 1000 genomes and Africa America Diabetes Mellitus (AADM) study: Recruited from academic medical centers in Nigeria, Ghana and Kenya, this cohort included people with ADA (American Diabetes Association) criteria - or treatment-confirmed type 2 diabetes. After quality control, they analyzed data from 1769 participants (1031 cases and 738 controls) genotyped with the Affymetrix Axiom PANAFR SNP array and imputed using the Burrows-Wheeler positional transformation method versus the Sanger imputation server.\u003c/p\u003e \u003cp\u003eGenetic instruments for kidney disease drugs\u003c/p\u003e \u003cp\u003eWe examined vascular Endothelial Growth Factor (VEGF) inhibitor drug class, encoded by the VEGFA gene (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). CLDN14 gene encodes a tight junction protein a class of drugs that target the tight junctions between cells(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).RHEB (Ras homologous enriched in the brain) a protein GTPase implicated in the inhibition of cell growth and inflammation RHEB is it\u0026rsquo;s encoding gene.SLC22A2 is a gene encoding the OCT2 protein(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These genes were selected as drug targets based on their known roles in pathways related to endothelial dysfunction, serum creatinine, fibrosis, inflammation, eGFR and decline in renal function, making them potential candidates for drug tailoring efforts in the context of type 2 diabetes and associated renal complications.\u003c/p\u003e \u003cp\u003eWe selected single nucleotide polymorphisms (SNPs) located within or nearby each coding gene (selected previously) and associated with eGFR at a genome-wide significance level (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8) (S1). We performed clumping to identify independent SNPs using the linkage disequilibrium between them. Clumping was used to prevent SNPs from being correlated with each other in our instruments. The idea is to retain the most representative SNPs for each region of linkage disequilibrium(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) because, when correlated, SNPs can influence each other's causal pathway.\u003c/p\u003e \u003cp\u003eTo ensure that the effect of a SNP on exposure and outcome corresponded to the same allele, we harmonized the data and excluded palindromic SNPs whose minor allele frequency was \u0026gt;\u0026thinsp;40% (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The F-statistic was determined for the different variants used as instruments to measure instrument strength based on the way earlier report by Burgess et al(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation about the drug targets and encoding genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug target\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEncoding Genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ee.g.,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHEB inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHEB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRapamycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChr7: 151,163,098\u0026ndash;151,217,206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLDN14 modulators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCLDN14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBerberine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChr21 : 37,832,919\u0026thinsp;\u0026minus;\u0026thinsp;37,948,867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGFA inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVEGFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAvastin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChr6 : 43,737,921\u0026thinsp;\u0026minus;\u0026thinsp;43,754,224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC22A2 inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC22A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCimetidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChr 6: 160,592,093\u0026ndash;160,698,670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStatistical methods\u003c/p\u003e \u003cp\u003eIn this two-sample MR analysis, the weighted inverse variance (WIV) approach was applied in the R package for Mendelian randomisation as the main analysis method, as it is the most suitable method to use with summarised data.(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSensitivity analyses\u003c/p\u003e \u003cp\u003eWe used the MR-Egger, weighted median, and weighted mode methods as sensitivity analyses, since these approaches are the most robust to the plausible pleiotropic effects of the variants used as instrumental variables(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). There are no horizontal pleiotropic effects when the MR-Egger intercept test is not statistically significant. If it is significant, it indicates that the IVW may be biased(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Although directionally consistent results from different methods may strengthen our conclusions, with MR showing evidence of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, colocalization analysis was used to test whether exposure-associated and target-drug-associated genetic variants were in the same genomic region and shared a common causal variant. On a Bayesian basis, we set the a priori probability of association between all the variants and one or other trait at 1 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;4 and the a priori probability of a causal variant shared between two traits at 1 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;5. (\"coloc\" R package)(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eGenetic instruments for kidney disease drug targets\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a synthesis of the instruments used for each exposure. F-statistics for all single variants range from 30.085 to 243.297, indicating a minimal risk of instrumenting and low bias (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstrumental variables for the exposure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian F (range)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHEB inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.84 (38.08-243.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLDN14 modulators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.02 (30.08\u0026ndash;37.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGFA inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.98 (35.1-96.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC22A2 inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.10 (41.22-127.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMendelian randomization analysis\u003c/p\u003e \u003cp\u003eWe identified strong support that both genetically predicted inhibition of VEGFA and inhibition of RHEB were associated with an increased incidence of T2DM (OR 2.66, 95% CI\u0026thinsp;=\u0026thinsp;1.34\u0026ndash;3.78, P\u0026thinsp;=\u0026thinsp;0.001) and (OR 2.25, 95% CI\u0026thinsp;=\u0026thinsp;1.34\u0026ndash;3.28, P\u0026thinsp;=\u0026thinsp;0.001) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). there was no evidence of an association between genetically predicted inhibition of SLC22A2 or inhibition of CLDN14 and the risk of T2DM (OR [95% CI] 0.95 [1.56\u0026ndash;1.48], P\u0026thinsp;=\u0026thinsp;0.84) (OR [95% CI] 0.95 [0.71\u0026ndash;2.20], P\u0026thinsp;=\u0026thinsp;0.84) respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable IVW Mendelian Randomization results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBETA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHEB inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,347 to 3,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLDN14 modulators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.712 to 2.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGFA inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.843 to 3.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC22A2 inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.615 to 1.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRHEB inhibitors, CLDN14 modulators; VEGFA inhibitors; SLC22A2 inhibitors; IVW, Inverse Variance Weighted; SE, standard error.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSensitivity analyses\u003c/p\u003e \u003cp\u003eAccording to the MR-Egger method, there was no significant evidence of heterogeneity between the genetic variants used as instrumental variables in the MR analysis (Q_pva\u0026thinsp;=\u0026thinsp;0.917); nor of significant evidence of directional pleiotropy with an intercept egger of 0.0799, p-value\u0026thinsp;=\u0026thinsp;0.194, indicating robust results in the sensitivity analyses.\u003c/p\u003e \u003cp\u003eColocalization analysis didn't show enough support for shared causal variant of genetically predicted inhibition of RHEB and VEGFA with our outcome data (PPshared\u0026thinsp;=\u0026thinsp;6.49%, and PPshared\u0026thinsp;=\u0026thinsp;11.02) respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we performed T2MR analyses to investigate associations between kidney disease drugs target and the risk of type 2 diabetes in the African population. We examined Vascular Endothelial Growth Factor (VEGF) inhibitor drug class. VEGFA is a protein that is important for the growth and development of blood vessels. VEGF/VEGFR inhibitors [for example, Bevacizumab (Avastin), Pazopanib (Votrient)] are a class of drugs that inhibit the action of vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor (VEGFR) (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) used to treat various types of cancer, including kidney cancer (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), VEGF inhibitors act by inhibiting angiogenesis through inhibition of the VEGF pathway. VEGF inhibitors belong to a group of drugs known as \"antiangiogenic agents\", working by preventing the growth of blood vessels that supply tumors with oxygen and nutrients(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results show a negative effect of Vascular Endothelial Growth Factor (VEGF) inhibitor drug class on type 2 diabetes risk. Our finding demonstrates that mTOR inhibitors like RHEB can be harmful for type 2 diabetic patients and increase its occurrence. RHEB is a protein GTPase implicated in the inhibition of cell growth and inflammation via its involvement in the mTORC1 (mechanistic target of rapamycin complex 1) signaling pathway (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Rapamycin, an mTOR inhibitor, reduces excessive interstitial inflammation, fibrosis and renal performance decline that occur at the onset of chronic kidney disease (CKD)(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also observed that the Claudin 14 (CLDN14) and SLC22A2 have no significant effect on diabetes risk. \u003cem\u003eCLDN14\u003c/em\u003e gene encodes a tight junction protein that plays a crucial role in preserving the glomerular filtration barrier in the kidney. The mechanism of the drug class targeting CLDN14 is that of tight junction modulators. Tight junction modulators (Claudin-1-specific peptide AT1002, Berberine) are a class of drugs that target the tight junctions between cells, which are responsible for maintaining the integrity of the epithelial barrier (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). These drugs can be used to treat of various conditions, including kidney disease, by improving tight junction function and decreasing the permeability of the intracellular epithelial barrier membrane(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSLC22A2 encoding the OCT2 protein, is known for its important role in the renal elimination of cationic drugs and endogenous organic cations (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e),(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The OCT2 protein is a polyspecific transporter that facilitates the absorption of a wide range of cationic drugs, including several used to treat kidney disease, such as cimetidine, famotidine, oxycodone, procainamide, and ranitidine(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). One study showed that SLC22A2 is associated with tubular creatinine secretion and distorted estimated glomerular filtration rate (eGFR) in kidney transplantation, (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results revealed evidence linking genetically predicted inhibition of vascular endothelial growth factor A (VEGFA) and brain-enriched Ras homolog (RHEB) to higher incidences of type 2 diabetes (T2DM) in Africans, whereas genetically predicted inhibition of SLC22A2 or inhibition of CLDN14 showed no association with T2DM risk.\u003c/p\u003e \u003cp\u003eIt should therefore be noted that drugs used to treat renal dysfunction can have a detrimental effect on type 2 diabetes and adversely affect the patient's health.\u003c/p\u003e \u003cp\u003eOur findings may be particularly significant for pre-diabetics and diabetics with renal dysfunction, for whom the preferential use of VEGFA and RHEB inhibitors for their treatment may have an additive impact on, or possibly worsen, the risk of developing diabetes compared to other therapies for renal dysfunction.\u003c/p\u003e \u003cp\u003eMendelian randomization studies rely on the assumption that genetic variants are not influenced by confounding factors, which may not always hold true (69,70,71,72). Additionally, the study focused on genetic variants in African populations but did not explore potential differences across subpopulations or consider other factors that may influence drug response(74, 75). Further research is needed to validate the findings of this study and explore additional genetic and non-genetic factors that may impact the effectiveness of repurposed drugs for type 2 diabetes in African populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we add to the existing breadth of research on the potential role of kidney disease medications in preventing type 2 diabetes mellitus (T2DM) through Mendelian randomisation. By incorporating the effects of genetic variants that have a significant impact on estimated glomerular filtration rate (eGFR) in individuals of African descent, alongside targeted drug therapies for kidney disorders, we uncover compelling evidence suggestive of a possible link between specific drug classes and an elevated risk for T2DM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACE: Angiotensin-converting enzyme\u003c/p\u003e\n\u003cp\u003eADA: American Diabetes Association\u003c/p\u003e\n\u003cp\u003eARB: Angiotensin II receptor blocker\u003c/p\u003e\n\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eCKD: Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eOAT: Organic anion transporters.\u003c/p\u003e\n\u003cp\u003eeGFR: Estimated glomerular filtration rate.\u003c/p\u003e\n\u003cp\u003eHbA1c: Glycated haemoglobin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSGLT2: Sodium-glucose cotransporter 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGFR: Glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eGWAS: Genome-wide association study\u003c/p\u003e\n\u003cp\u003eMR: Mendelian randomization\u003c/p\u003e\n\u003cp\u003eIDF: \u0026nbsp;International Diabetes Federation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCT: Randomized controlled trial.\u003c/p\u003e\n\u003cp\u003eSLC22A1: Solute-like carrier family 22 member 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSLC22A: Solute-like carrier family 22 members.\u003c/p\u003e\n\u003cp\u003eSLC22A2: Solute-like carrier family 22 member 2.\u003c/p\u003e\n\u003cp\u003eSLC22A3: Solute-like carrier family 22 member 3\u003c/p\u003e\n\u003cp\u003eCKDGen: Chronic Kidney Disease genetics.\u003c/p\u003e\n\u003cp\u003eVEGFA: vascular endothelial growth factor A\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRHEB: Ras homolog enriched in brain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM: Type 2 diabetes mellitus.\u003c/p\u003e\n\u003cp\u003eSNP: Single nucleotide polymorphism.\u003c/p\u003e\n\u003cp\u003eUK Biobank (UKBB).\u003c/p\u003e\n\u003cp\u003eMVP: Million Veteran Program.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAADM: The Africa America Diabetes Mellitus (AADM)\u003c/p\u003e\n\u003cp\u003eIVW: Inverse variance weighted.\u003c/p\u003e\n\u003cp\u003eVEGFR: vascular endothelial growth factor receptor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emTOR: \u0026nbsp;mammalian target of rapamycin\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOS is supported by the African Research Excellence Fund (AREF-325-SORE-F-C0904). SF is supported by Wellcome Trust grant 220740/Z/20/Z. AD is supported by a grant from the Fogarty International Centre and the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under number U2RTW010673 for the West African Centre of Excellence for Bioinformatics Research Training in Global Health through the African Centre of Excellence in Bioinformatics in Bamako.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Million Veteran Program (MVP) staff, researchers, and volunteers, who have contributed to MVP, and especially participants who previously served their country in the military and now generously agreed to enroll in the study. (See https://www.research.va.gov/mvp/ for more details). The citation for MVP is Gaziano, J.M. et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J Clin Epidemiol 70, 214-23 (2016). This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by the Veterans Administration (VA) Cooperative Studies Program (CSP) award #G002. \u0026ldquo;Data was accessed through approved dbGaP proposal #30287 entitled, \u0026ldquo;Genomic determinant of Complex Diseases in African ancestry individuals\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSF and OS conceptualized the study. AD carried out the analyses. OS, MT and SF checked the underlying data. AD, OS, MT, and SF drafted the first version of the manuscript. SF, CK, MT and OS interpreted the results. KT, OD, ON, SOD, TYA, JGS and MD interpreted the data. AAS, JOA, OS, MT, OD, CC, MW, ON, SOD, JGS, TYA, MD, SF, made critical revisions and modifications to the manuscript. All authors have read and approved the final manuscript. SF is the guarantor of this work.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study were obtained from the following sources:\u003c/p\u003e\n\u003cp\u003eMVP dataset: This dataset was accessed through the dbGaP database under proposal number #30287.UKBB eGFRcrea summary statistics: This dataset was downloaded from the UK Biobank using phenocode 30700. CKDGen dataset: This dataset was downloaded from the CKDGen consortium website. (https://ckdgen.imbi.uni-freiburg.de/datasets/Gorski_2021 ).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM GWAS summary statistics: available to download from the EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/31049640 )\u003c/p\u003e\n\u003cp\u003eAll scripts are available on request from corresponding authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe co-authors declared no potential conflict of interest in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical consideration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo ethical approval is required for this research as the data used are summary data are publicly available unless otherwise stated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOgurtsova K, Guariguata L, Barengo NC, Ruiz PLD, Sacre JW, Karuranga S, et al. 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Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee K, Lim CY. Mendelian Randomization Analysis in Observational Epidemiology. J Lipid Atheroscler. 2019;8(2):67\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGill D, Georgakis MK, Walker VM, Schmidt AF, Gkatzionis A, Freitag DF, et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res. 2021;6:16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith GD, Ebrahim S. Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies. In: Biosocial Surveys [Internet]. National Academies Press (US); 2008 [cited 2023 Nov 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK62433/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK62433/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, drug repurposing, kidney disease, estimated glomerular filtration rate, Mendelian randomization, Africa","lastPublishedDoi":"10.21203/rs.3.rs-3956597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3956597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the growing number of treatments available for diabetics, many people fail to achieve their therapeutic goals. The complexity of diabetes prevention and control exacerbates the situation in low-income countries. These complexities include genetic factors, social, and financial burdens. Strategies for optimizing coverage for new drugs and clinical therapies for type 2 diabetes mellitus (T2DM) have focused on dual-use approaches for new or off-label indications. This study aimed to determine whether inhibition of kidney function drug targets have adverse effect on T2DM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A two-sample Mendelian randomization (MR) study was conducted based on genetic variants located in or near genes (in 300 kilobyte windows) for encoding significant drug targets. We used summary statistics of eGFR GWAS (n=80,027) of African ancestry individuals and GWAS datasets of T2DM (n=4,347 Africans in South Africa, Nigeria, Ghana and Kenya), to predict the effects of drug exposure on T2DM risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eGenetically predicted inhibition of vascular endothelial growth factor A (VEGFA) and Ras homolog enriched in brain (RHEB) were associated with higher odds of T2DM incidence (OR, 2.66; 95% CI 1.34–3.78, and OR, 2.25; 95% CI, 1.34–3.28, respectively). Genetically predicted inhibition of SLC22A2 and inhibition of CLDN14 were not associated with T2DM occurrence (OR, 0.95; 95% CI, 0.61-1.48 and OR, 1.56; 0.71–2.20, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e: Our results suggest VEGFA inhibitors and RHEB inhibitors drugs may increase the risk or exacerbate T2DM risk in Africans, hence a need for closely monitoring the safety and efficacy of anti-diabetic drugs in the African population.\u003c/p\u003e","manuscriptTitle":"Genetically proxied therapeutic inhibition of kidney function drug targets and type 2 diabetes in Africans: A Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 15:49:13","doi":"10.21203/rs.3.rs-3956597/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c10518af-61ab-409d-baa1-36294db8409c","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-15T07:06:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 15:49:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3956597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3956597","identity":"rs-3956597","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}