Geographic Variation in Cardiovascular Disease Prevention by GLP-1 Receptor Agonists and SGLT2 Inhibitors: A Meta-Analysis

Geographic Variation in Cardiovascular Disease Prevention by GLP-1 Receptor Agonists and SGLT2 Inhibitors: A Meta-Analysis | 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 Article Geographic Variation in Cardiovascular Disease Prevention by GLP-1 Receptor Agonists and SGLT2 Inhibitors: A Meta-Analysis Jacob Ilany, Ohad Cohen, Malka Gorfine This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6863965/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 Based on cardiovascular outcome trials, the FDA has approved the indication to reduce the risk of cardiovascular events in patients with diabetes for GLP-1 receptor agonists and SGLT-2 inhibitors. We assess the effect of GLP-1 receptor agonists and SGLT-2 inhibitors on cardiovascular risk reduction according to geographic areas. We performed a systematic search of cardiovascular outcome trials in PubMed (MEDLINE) until September 2024. We included cardiovascular outcome trials with these medications that demonstrated a reduction in major cardiovascular events in patients with diabetes and provided geographic sub-analyses of these outcomes. The data was extracted from the study publications, including their supplementary material. A statistical meta-analysis was conducted to assess differences in cardiovascular risk reduction by region. The primary outcome was the regional variation in cardiovascular risk reduction, as measured by major cardiovascular events, associated with these medications. We found 5 studies with GLP-1 receptor agonists and 2 with SGLT-2 inhibitors in which a reduction in major cardiovascular events was recorded and related geographic sub-analysis was published. None of the studies demonstrated major cardiovascular events improvement in North America. Almost all the studies found higher hazard ratio for major cardiovascular events in North America as compared to whole study hazard ratio. We conclude that GLP-1 receptor agonists and SGLT-2 inhibitors do not decrease cardiovascular risk in diabetic patients in North America. Based on our results, the FDA should reconsider the registered indication of cardiovascular prevention in patients with diabetes mellitus for these medications. Health sciences/Endocrinology/Endocrine system and metabolic diseases Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases Figures Figure 1 Introduction The main challenge in managing diabetes mellitus (DM) lies in mitigating the complications associated with it. While improved glycemic control is effective in reducing the risk of microvascular complications, such as diabetic retinopathy, nephropathy and neuropathy, improving glycemic control by itself may not sufficiently prevent macrovascular complications such as cardiovascular (CV) events. Studies designed to assess whether a more aggressive glucose control would prevent cardiovascular disease have failed to demonstrate improvement 1 , 2 . As diabetes is a major contributor to cardiovascular disease, attempts to identify anti-hyperglycemic medications that would also lower the risk of heart disease in patients with DM are ongoing. Following the issue regarding the cardiovascular safety of the anti-DM drug rosiglitazone 3 , the U.S. Food and Drug Administration (FDA), in 2008, published a requirement that CV safety studies be conducted on all new diabetes medications prior to approval 4 . In recent years, some optimism arose as, based on these cardiovascular outcome trials (CVOTs), the FDA registered two classes of anti-DM drugs for use under the indication of CV risk reduction in patients with DM. The first class is Glucagon-Like-Peptide-1 (GLP-1) receptor agonists. Drugs from this class are relatively effective at treating hyperglycemia and lead to weight loss. Exenatide, the first drug from this class, was approved for the treatment of diabetes in 2005. In 2017, after publishing the LEADER study results 5 , the FDA approved Liraglutide for the indication of CV prevention in people with DM with established heart disease 6 . CV risk reduction labelling was issued for Dulaglutide (with and without known heart disease) and Semaglutide in 2020. The second class of medication is the Sodium-Glucose-Transporter-2 (SGLT2) receptor inhibitors. These drugs prevent the reabsorption of glucose in the renal tubules, resulting in the excretion of glucose in the urine. They were developed primarily for the treatment of diabetes. However, while this class of medication was of limited effectiveness for the treatment of hyperglycemia, it was claimed to be effective in preventing heart disease, treating heart failure, and treating and preventing various types of kidney disease 7 . In 2016, following the publication of the EMPA-REG study 8 , the FDA approved the use of Empagliflozin to reduce CV death in DM patients 9 . This was followed by Canagliflozin (to reduce major cardiovascular events (MACE)) in 2018. More recently, doubts were raised regarding the efficacy of SGLT2is for CV and renal diseases 10 – 12 . To approve a new drug or indication, the FDA requires evidence demonstrating the drug’s efficacy in the target population, those who will ultimately use the medication. The FDA also encourages ensuring that different subgroups within the target population are adequately represented 13 . In this study, we aimed to evaluate the CV protective effects of GLP-1 receptor agonists and SGLT2is in patients with type 2 DM stratified by geographic region. By doing so, we sought to determine whether the observed benefits in CV risk reduction apply uniformly across regions, particularly in North America, which is the target population for the FDA. Methods This meta-analysis is reported in line with the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) 14 statement and was registered on the international Prospective Register of Systematic Reviews (PROSPERO, CRD42024601754). A PRISMA checklist table can be found in the Supplement. Search Strategy and Study Selection We reviewed PubMed (Medline) and identified all controlled studies that contained cardiovascular products conducted with GLP-1 agonists and SGLT2 inhibitors in patients with DM in which there was a follow-up of more than one year. The review in PubMed was carried out by running "glucagon like peptide 1 cardiovascular" and "sodium glucose transporter 2 cardiovascular" with a filter of "clinical trial." We further selected all the studies that fit the criteria, including MACE as outcome, and provided information on the geographic distribution of the results. We include in the analysis all the studies that meet the above criteria and demonstrated a significant decrease in MACE in patients treated with the drugs. MACE was defined based on the original definition, i.e., CV death, non-fatal myocardial infarction and non-fatal stroke. Statistical Analysis A statistical meta-analysis was performed using the summary statistics regarding the geographic distribution of all eligible studies from the identified studies. Each study applied Cox regression analysis to assess the time to major adverse cardiovascular events and reported hazard ratios (for either GLP-1 agonist or SGLT2 inhibitor treatments) along with 95% confidence intervals, for all patients and by region. The overall hazard ratio (HR) is defined as the log of a weighted average of the individual log HRs, with the weights inversely proportional to the variance of the log HR of each trial. The analysis was conducted using R, following the method described by Parmar et al. 15 to extract summary statistics for meta-analysis based on survival outcomes and reported confidence intervals. Role of funding source No funding was given to this work. Results Selection of studies Figure 1 presents the flowchart of the study selection process. For GLP-1 agonists: Nine studies met the selection criteria. Three of the studies (ELIXA 16 , EXSCEL 17 and PIONEER-6 18 did not show a decrease in the incidence of MACE with treatment. Five of them (LEADER 5 , SUSTAIN-6 19 , HARMONY OUTCOMES 20 , REWIND 21 , and AMPLITUDE-O 22 ) demonstrated a significant decrease in the incidence of MACE and were included in the analysis. One recent study, the FLOW trial with Semaglutide 23 , found improved MACE but the geographical sub-analysis was related only to the primary outcome (cardiorenal) rather than to MACE. For SGLT2 inhibitors: Six out of 12 cardiovascular or cardiorenal outcome studies with follow up for more than one year were carried out on people with DM. The other six studies (DAPA-HF 24 , EMPEROR-Reduced 25 , EMPEROR-Preserved 26 , DELIVER 27 , DAPA-CKD 28 and EMPA-Kidney 29 ) were not conducted on people with DM and did not publish results regarding MACE, and were therefore excluded from this analysis. In four out of the six studies analyzed, an improvement in the incidence of MACE was demonstrated with drug therapy. In two studies (EMPA-REG 8 and CANVAS 30 ), the geographic distribution of the MACE result was published and these two studies were included in the meta-analysis. In two studies (DECLARE-TIMI 31 and VERTIS-CV 32 ), there was no improvement in MACE incidence. In the other two studies in which a significant decrease in the incidence of MACE was found (SCORED 33 and CREDENCE 34 ), the geographic distribution regarding MACE was not available. MACE outcome by geographic regions In both drug classes, none of the studies found a statistically significant decrease in the incidence of MACE in the North American region (Table 1 , A and B). Table 1 Results of the studies by geographic sub-analysis Study Medication Type of medication Population N Length (Years) Main outcome Total study results Geographic areas results Ref. North America Europe Asia South America Africa Other HR No. (%) HR No. (%) HR No. (%) HR No. (%) HR No. (%) HR No. (%) HR A LEADER Liraglutide GLP-1 agonist DM + CV risk 9340 3.8 MACE 0.87 30 1.01 35 0.82 8 0.62 27 0.83 5 REWIND Delaglutide GLP-1 agonist DM + CV or CV risk 9901 5.4 MACE 0.88 21 1.14 44 0.77 5 0.54 30 0.99 21 SUSTAIN-6 Semaglutide GLP-1 agonist DM 3297 2 MACE 0.74 34 0.87 21 0.62 45 0.68 19 HARMONY OUTCOMES Albiglutide GLP-1 agonist DM + CV 9463 1.6 MACE 0.78 21 0.92 57 0.73* 4 0.69 18 0.76 20 AMPLITUDE-O Efpeglenatide GLP-1 agonist DM + (CV or KD + CV risk) 4076 1.8 MACE 0.73 26.5 1.20 31.5 0.59 22.7 0.44 19.3 0.79 22 B CANVAS Canagliflozin SGLT2 inhibitor DM + CV risk 10142 3.5 MACE 0.86 0.84 0.8 0.84 0.94 30 EMPA-REG Empagliflozin SGLT2 inhibitor DM 7020 3.1 MACE 0.86 20 0.89 41 1.02 19.2 0.7 15.4 0.58 4.4 0.86 8 C SCORED Sotagliflozin SGLT2 inhibitor DM + CKD + CV risk 10584 1.3 CV death + admin for HF + acute HF 0.74 16 0.92 35 0.72 35 0.77 14 0.68 33 CREDENCE Canagliflozin SGLT2 inhibitor DM + CKD 4401 2.6 Composite renal + CV death 0.7 27 0.84 20 0.82 21 0.61 32 0.58 34 D FLOW Semaglutide GLP-1 agonist DM + CKD 3533 3.4 Composite renal + CV death 0.76 25 0.98 27 0.61 26 0.85 22 0.62 23 E EMPA-REG Empagliflozin SGLT2 inhibitor DM 7020 3.1 CV death 0.62 20 0.81 41 0.72 19.2 0.35 15.4 0.43 4.4 0.8 8 F SELECT Semaglutide GLP-1 agonist Obesity + CV 17604 2.8 MACE 0.8 25 0.92 38 0.69 12.5 0.71 24.5 0.88 40 G FIGARO-DKD Finerenone Mineralo corticoid receptor antagonist DM + CKD 7437 3.4 MACE including admin for HF 0.87 15 0.88 47 0.95 22 0.79 11 0.65 4 0.87 41 FINEARTS-HF Finerenone Mineralo corticoid receptor antagonist Heart Failure 6001 2.7 CV death + admin for HF + acute HF 0.84 8 0.98 65 0.83 16 0.95 11 0.65 42 “No. %” means the percentage of study participants in the specific geographic area out of the total study population. HR – Hazard Ratio. All HRs that are statistically significant results appear in bold letters. Otherwise, the results are not significantly different. The sections of the table: A – Studies with GLP-1 agonists, B – Studies with SGLT-2 inhibitors, C – SCORES and CREDENCE studies results for the primary outcome, D – FLOW study results for the primary outcome, E - EMPA-REG study results for CV mortality, F – SELECT study results (not DM patients), G – Studies with Finerenone. * Estimation HR for Europe based on the results in east and west Europe. The meta-analysis that was performed based on seven studies demonstrates an overall MACE hazard ratio (95% confidence interval) of 0.84 (0.80–0.89). The following are the stratified total hazard ratios by region: North America (7 studies): 0.97 (0.88–1.08); Europe (7 studies): 0.80 (0.73–0.87); Asia (4 studies): 0.64 (0.50–0.82); South America (5 studies): 0.82 (0.70–0.95); other (4 studies): 0.84 (0.73–0.96). The meta-analysis revealed that the HR for MACE in North America was significantly higher compared to the overall HR in the study populations. In contrast, in many cases the HR was significantly lower than the general study risk ratio in other geographic regions, dragging the overall result to a significant decrease in MACE. Two SGLT2 studies with a significant decrease in MACE (SCORED 33 and CREDENCE 34 ) did not report the geographic distribution regarding MACE and thus were excluded from the meta-analysis. The primary outcome examined in these studies were cardiovascular mortality and hospitalizations/urgent visits due to heart failure in SCORED, and cardiovascular or renal mortality and measures of renal deterioration in CREDENCE. Even with regard to these results, for which a very significant decrease in risk was demonstrated by the treatment (HR of 0.74 and 0.7 respectively), the hazard reduction was not demonstrated in North America (Table 1 C). Similarly, the recent FLOW trial with the GLP-1 agonist Semaglutide demonstrates improved MACE and even total and CV mortality 23 . Nevertheless, the primary cardiorenal outcome that improved even more (24% reduction) did not improve at all in North America (Table 1 D). Discussion Cardiovascular diseases are the leading causes of morbidity and mortality in individuals with type 2 DM 35 . Thus, it is very important to prevent these complications in these patients. Most of the anti-hyperglycemic drugs have not proven effective in preventing CV diseases. The FDA requires CV outcome studies on any new DM medication before registration. The purpose is to prove safety, i.e., that the drug does not increase the incidence of CV complications. Therefore, many large controlled CV outcome studies have been conducted in recent years. All the studies done with GLP-1 agonists and SGLT2 inhibitors actually demonstrated good CV safety, i.e., these drugs do not increase the risk of CV diseases in patients with DM. A significant reduction in MACE was demonstrated in some studies with GLP-1 agonists and in a minority of studies conducted with SGLT2 inhibitors. Based on these studies, the FDA added the indication of CV disease prevention in DM patients to the registries of these drugs. However, we have noticed a consistent pattern of geographical differences in the CV outcomes in these studies. We therefore aimed to methodologically examine all CV outcome studies that demonstrated an improvement in CV outcomes (defined as MACE) with these drugs for the regional effects in said outcomes. We found five such studies for GLP-1 agonists and two for SGLT2 inhibitors in which the geographic distribution of the MACE results were published. We found that our initial hypothesis for regional differences in the CV effects of GLP-1 agonists and SGLT2 inhibitors has been substantiated. Consistently, the results in North America were different from the overall result of the studies examined. No significant reduction in the incidence of MACE was demonstrated in North America. The overall positive effect on CV outcomes were dominated by the significant decrease in MACE incidence in other parts of the world. This phenomenon has been noticed before, for example, by Kang et al. 36 . It led them to conclude that GLP-1 agonists are more effective for CV prevention in the Asian population. With regard to SGLT2 inhibitors, we should mention that most of the CVOTs do not show a decrease in MACE. Our study only tested this with respect to the studies that demonstrated an improvement. Furthermore, in most of the CVOTs done both with GLP-1 agonists and SGLT2 inhibitors, CV mortality and all-cause mortality did not improve with treatment (5 of 9 for GLP-1 and 9 of 12 for SGLT2). Even in the study with the most impressive mortality reduction, EMPA-REG 8 (38% reduction in CV mortality), it did not occur in North America (Table 1E). Geographic variation in large clinical trials is a known phenomenon. There are different characteristic in different populations that might affect the study outcomes. For example, geographic variations were reported in the TECOS 37 and EXSCEL 38 trials. The NAVIGATOR trial 39 examined the effect of Nateglinide and Valsartan in patients with impaired glucose tolerance. Although major regional differences were found regarding CV outcomes, the response to the medical treatment did not differ by geographic areas. By contrast, in the trials we analyzed in our study, different responses to medical treatment were found between different geographic areas. These variances were found for GLP-1 agonists as well as SGLT2 inhibitors, two classes of medication with completely different mechanisms of action. The different response to treatment was not in correlation with ethnic origin 5, 11 , ruling out ethnicity as a possible explanation. Furthermore, the “North American phenomenon” can be found with other study populations and other medications as well. For example, it was found in the SELECT study 40 , a study with the GLP-1 agonist Semaglutide in obese patients without DM (Table 1F), as well as in FIGARO-DKD 41 and FINEARTS-HF 42 , trials conducted with Finerenone, a mineralocorticoid receptor antagonist (Table 1G). Regional differences in baseline treatment might possibly influence CV outcomes. For example, the fewer preventive approaches applied in a region, the more effective these drugs might be. This option requires further investigation, as no data was clearly provided in the studies. In fact, we could not identify any study that reported a difference in baseline treatment by geographic region. The concern of the applicability of data with regional differences on decisions for the North American population was already raised by Dr. Budnitz when he voted against Liraglutide approval for CV prevention in 2017 6 . He then had only the results of one trial. The FDA requires that the effectiveness of the drugs it approves be investigated in the target population of said drugs. Indeed, participants from the North American region, the primary target population for FDA-approved drugs, took part in all the trials included in our analysis. However, their CV risk did not improve with treatment. Clearly, none of the studies demonstrated significant improvement in MACE with treatment in the North American region. Limitations The main limitation of our study is that we cannot be sure that the size of the groups in North America is large enough to be powered to show independent superiority. However, this limitation does not prevent our conclusion, as: 1. The North American HR is clearly and consistently higher and different from the total HR; 2. Smaller groups in other geographical areas reach statistical significance and their HRs are lower than the total HRs; 3. Even if the North American groups are too small to demonstrate MACE improvement by themselves, we can at least declare that prevention of CV disease (as reflected by MACE) by these medications has not been proven in North America. Conclusions Even in those trials in DM patients with GLP-1 agonist and SGLT2 inhibitors that demonstrate significant improvement in MACE incidence, said improvement did not occur in North America. As North America is the primary target population for the FDA, it should reevaluate and reconsider the registration of the indication “Disorder of CV System: Prophylaxis – Type 2 DM” for these medications. Declarations Funding: No funding was given to this study. Acknowledgments The authors thanks Mr. David Dvash for his valuable help in preparing the manuscript. Conflict of interests: The author declare no competing interests. References Ismail-Beigi F, Craven T, Banerji MA, et al. Effect of intensive treatment of hyperglycaemia on microvascular outcomes in type 2 diabetes: an analysis of the ACCORD randomised trial. Lancet 2010; 376 : 419-430. Patel A, MacMahon S, Chalmers J, et al. The ADVANCE Collaborative Group. Intensive Blood Glucose Control and Vascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med 2008; 358 : 2560-2572. Nissen SE, Wolski K. Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. 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Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N Engl J Med. 2023; 389 : 2221-2232. Pitt B, Filippatos G, Agarwal R, et al. Cardiovascular Events with Finerenone in Kidney Disease and Type 2 Diabetes. N Engl J Med. 2021; 385 : 2252-2263. Solomon SD, McMurray JJV, Vaduganathan M, et al. Finerenone in Heart Failure with Mildly Reduced or Preserved Ejection Fraction. N Engl J Med. 2024; 391 : 1475-1485. Additional Declarations There is NO Competing Interest. Supplementary Files Supplement.docx Supplement 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. 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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-6863965","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493785106,"identity":"39feef31-5c97-4132-9a18-678d65204f32","order_by":0,"name":"Jacob Ilany","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-9208-3648","institution":"Sheba Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Ilany","suffix":""},{"id":493785107,"identity":"dc80fd20-46be-4972-bbc1-a3030b2ee6d8","order_by":1,"name":"Ohad Cohen","email":"","orcid":"","institution":"Medtronic International Trading Sàrl","correspondingAuthor":false,"prefix":"","firstName":"Ohad","middleName":"","lastName":"Cohen","suffix":""},{"id":493785108,"identity":"bcb2b5de-2126-44e7-9df5-425f3544603d","order_by":2,"name":"Malka Gorfine","email":"","orcid":"","institution":"Department of statistics and operations research, Tel Aviv University, Israel","correspondingAuthor":false,"prefix":"","firstName":"Malka","middleName":"","lastName":"Gorfine","suffix":""}],"badges":[],"createdAt":"2025-06-10 14:10:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6863965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6863965/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88222017,"identity":"d736d96e-2628-4611-bb3b-7a42e7985f48","added_by":"auto","created_at":"2025-08-04 08:02:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29154,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flowchart\u003c/p\u003e\n\u003cp\u003eFlowchart of the inclusion of studies in the meta-analysis.\u003c/p\u003e\n\u003cp\u003eDM - Diabetes Mellitus, MACE – Major Adverse Cardiovascular Events.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6863965/v1/2600c0a685961ec27807fd8f.png"},{"id":88623972,"identity":"0aa8adc0-9394-4b6c-aa67-54093254e504","added_by":"auto","created_at":"2025-08-08 12:29:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":679329,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6863965/v1/ea8d4e61-a698-4917-98c0-0bfd60339610.pdf"},{"id":88222014,"identity":"93b94a9b-5ab3-481e-9dad-19954a7b959c","added_by":"auto","created_at":"2025-08-04 08:02:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19644,"visible":true,"origin":"","legend":"Supplement","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-6863965/v1/f071303f13418425224f429a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Geographic Variation in Cardiovascular Disease Prevention by GLP-1 Receptor Agonists and SGLT2 Inhibitors: A Meta-Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe main challenge in managing diabetes mellitus (DM) lies in mitigating the complications associated with it. While improved glycemic control is effective in reducing the risk of microvascular complications, such as diabetic retinopathy, nephropathy and neuropathy, improving glycemic control by itself may not sufficiently prevent macrovascular complications such as cardiovascular (CV) events. Studies designed to assess whether a more aggressive glucose control would prevent cardiovascular disease have failed to demonstrate improvement\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. As diabetes is a major contributor to cardiovascular disease, attempts to identify anti-hyperglycemic medications that would also lower the risk of heart disease in patients with DM are ongoing.\u003c/p\u003e\u003cp\u003eFollowing the issue regarding the cardiovascular safety of the anti-DM drug rosiglitazone\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, the U.S. Food and Drug Administration (FDA), in 2008, published a requirement that CV safety studies be conducted on all new diabetes medications prior to approval\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In recent years, some optimism arose as, based on these cardiovascular outcome trials (CVOTs), the FDA registered two classes of anti-DM drugs for use under the indication of CV risk reduction in patients with DM.\u003c/p\u003e\u003cp\u003eThe first class is Glucagon-Like-Peptide-1 (GLP-1) receptor agonists. Drugs from this class are relatively effective at treating hyperglycemia and lead to weight loss. Exenatide, the first drug from this class, was approved for the treatment of diabetes in 2005. In 2017, after publishing the LEADER study results\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, the FDA approved Liraglutide for the indication of CV prevention in people with DM with established heart disease\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. CV risk reduction labelling was issued for Dulaglutide (with and without known heart disease) and Semaglutide in 2020.\u003c/p\u003e\u003cp\u003eThe second class of medication is the Sodium-Glucose-Transporter-2 (SGLT2) receptor inhibitors. These drugs prevent the reabsorption of glucose in the renal tubules, resulting in the excretion of glucose in the urine. They were developed primarily for the treatment of diabetes. However, while this class of medication was of limited effectiveness for the treatment of hyperglycemia, it was claimed to be effective in preventing heart disease, treating heart failure, and treating and preventing various types of kidney disease\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In 2016, following the publication of the EMPA-REG study\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, the FDA approved the use of Empagliflozin to reduce CV death in DM patients\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This was followed by Canagliflozin (to reduce major cardiovascular events (MACE)) in 2018. More recently, doubts were raised regarding the efficacy of SGLT2is for CV and renal diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo approve a new drug or indication, the FDA requires evidence demonstrating the drug’s efficacy in the target population, those who will ultimately use the medication. The FDA also encourages ensuring that different subgroups within the target population are adequately represented\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to evaluate the CV protective effects of GLP-1 receptor agonists and SGLT2is in patients with type 2 DM stratified by geographic region. By doing so, we sought to determine whether the observed benefits in CV risk reduction apply uniformly across regions, particularly in North America, which is the target population for the FDA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis meta-analysis is reported in line with the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e statement and was registered on the international Prospective Register of Systematic Reviews (PROSPERO, CRD42024601754). A PRISMA checklist table can be found in the Supplement.\u003c/p\u003e\u003cp\u003eSearch Strategy and Study Selection\u003c/p\u003e\u003cp\u003eWe reviewed PubMed (Medline) and identified all controlled studies that contained cardiovascular products conducted with GLP-1 agonists and SGLT2 inhibitors in patients with DM in which there was a follow-up of more than one year. The review in PubMed was carried out by running \"glucagon like peptide 1 cardiovascular\" and \"sodium glucose transporter 2 cardiovascular\" with a filter of \"clinical trial.\"\u003c/p\u003e\u003cp\u003eWe further selected all the studies that fit the criteria, including MACE as outcome, and provided information on the geographic distribution of the results. We include in the analysis all the studies that meet the above criteria and demonstrated a significant decrease in MACE in patients treated with the drugs. MACE was defined based on the original definition, i.e., CV death, non-fatal myocardial infarction and non-fatal stroke.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eA statistical meta-analysis was performed using the summary statistics regarding the geographic distribution of all eligible studies from the identified studies. Each study applied Cox regression analysis to assess the time to major adverse cardiovascular events and reported hazard ratios (for either GLP-1 agonist or SGLT2 inhibitor treatments) along with 95% confidence intervals, for all patients and by region. The overall hazard ratio (HR) is defined as the log of a weighted average of the individual log HRs, with the weights inversely proportional to the variance of the log HR of each trial. The analysis was conducted using R, following the method described by Parmar et al.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e to extract summary statistics for meta-analysis based on survival outcomes and reported confidence intervals.\u003c/p\u003e\u003cp\u003eRole of funding source\u003c/p\u003e\u003cp\u003eNo funding was given to this work.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSelection of studies\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the flowchart of the study selection process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor GLP-1 agonists: Nine studies met the selection criteria. Three of the studies (ELIXA\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, EXSCEL\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and PIONEER-6\u003csup\u003e18\u003c/sup\u003e did not show a decrease in the incidence of MACE with treatment. Five of them (LEADER\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, SUSTAIN-6\u003csup\u003e19\u003c/sup\u003e, HARMONY OUTCOMES\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, REWIND\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and AMPLITUDE-O\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e) demonstrated a significant decrease in the incidence of MACE and were included in the analysis. One recent study, the FLOW trial with Semaglutide\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, found improved MACE but the geographical sub-analysis was related only to the primary outcome (cardiorenal) rather than to MACE.\u003c/p\u003e\u003cp\u003eFor SGLT2 inhibitors: Six out of 12 cardiovascular or cardiorenal outcome studies with follow up for more than one year were carried out on people with DM. The other six studies (DAPA-HF\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, EMPEROR-Reduced\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, EMPEROR-Preserved\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, DELIVER\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, DAPA-CKD\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and EMPA-Kidney\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e) were not conducted on people with DM and did not publish results regarding MACE, and were therefore excluded from this analysis. In four out of the six studies analyzed, an improvement in the incidence of MACE was demonstrated with drug therapy. In two studies (EMPA-REG\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and CANVAS\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e), the geographic distribution of the MACE result was published and these two studies were included in the meta-analysis. In two studies (DECLARE-TIMI\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and VERTIS-CV\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e), there was no improvement in MACE incidence. In the other two studies in which a significant decrease in the incidence of MACE was found (SCORED\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and CREDENCE\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e), the geographic distribution regarding MACE was not available.\u003c/p\u003e\u003cp\u003eMACE outcome by geographic regions\u003c/p\u003e\u003cp\u003eIn both drug classes, none of the studies found a statistically significant decrease in the incidence of MACE in the North American region (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, A and B).\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\u003eResults of the studies by geographic sub-analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"22\"\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eStudy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMedication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eType of medication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLength (Years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMain outcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal study results\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"12\" nameend=\"c21\" namest=\"c10\"\u003e\u003cp\u003eGeographic areas results\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c22\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eNorth America\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eEurope\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003eAsia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003eSouth America\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u003cp\u003eAfrica\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNo. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eNo. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eNo. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003eNo. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003eNo. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c20\"\u003e\u003cp\u003eNo. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c21\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLEADER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLiraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLP-1 agonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;CV risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREWIND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDelaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLP-1 agonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;CV or CV risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.77\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSUSTAIN-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLP-1 agonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHARMONY OUTCOMES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlbiglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLP-1 agonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;CV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.73*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAMPLITUDE-O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEfpeglenatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLP-1 agonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM + (CV or KD\u0026thinsp;+\u0026thinsp;CV risk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e31.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e19.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCANVAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCanagliflozin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSGLT2 inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;CV risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEMPA-REG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmpagliflozin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSGLT2 inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e15.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCORED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSotagliflozin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSGLT2 inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;CKD\u0026thinsp;+\u0026thinsp;CV risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCV death\u0026thinsp;+\u0026thinsp;admin for HF\u0026thinsp;+\u0026thinsp;acute HF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCREDENCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCanagliflozin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSGLT2 inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;CKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eComposite renal\u0026thinsp;+\u0026thinsp;CV death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLOW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLP-1 agonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;CKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eComposite renal\u0026thinsp;+\u0026thinsp;CV death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEMPA-REG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmpagliflozin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSGLT2 inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCV death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e15.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSELECT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLP-1 agonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eObesity\u0026thinsp;+\u0026thinsp;CV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFIGARO-DKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFinerenone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMineralo corticoid receptor antagonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;CKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMACE including admin for HF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFINEARTS-HF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFinerenone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMineralo corticoid receptor antagonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHeart Failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCV death\u0026thinsp;+\u0026thinsp;admin for HF\u0026thinsp;+\u0026thinsp;acute HF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"22\"\u003e\u0026ldquo;No. %\u0026rdquo; means the percentage of study participants in the specific geographic area out of the total study population.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"22\"\u003eHR \u0026ndash; Hazard Ratio. All HRs that are statistically significant results appear in \u003cb\u003ebold\u003c/b\u003e letters. Otherwise, the results are not significantly different.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"22\"\u003eThe sections of the table: A \u0026ndash; Studies with GLP-1 agonists, B \u0026ndash; Studies with SGLT-2 inhibitors, C \u0026ndash; SCORES and CREDENCE studies results for the primary outcome, D \u0026ndash; FLOW study results for the primary outcome, E - EMPA-REG study results for CV mortality, F \u0026ndash; SELECT study results (not DM patients), G \u0026ndash; Studies with Finerenone.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"22\"\u003e* Estimation HR for Europe based on the results in east and west Europe.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe meta-analysis that was performed based on seven studies demonstrates an overall MACE hazard ratio (95% confidence interval) of 0.84 (0.80\u0026ndash;0.89). The following are the stratified total hazard ratios by region: North America (7 studies): 0.97 (0.88\u0026ndash;1.08); Europe (7 studies): 0.80 (0.73\u0026ndash;0.87); Asia (4 studies): 0.64 (0.50\u0026ndash;0.82); South America (5 studies): 0.82 (0.70\u0026ndash;0.95); other (4 studies): 0.84 (0.73\u0026ndash;0.96).\u003c/p\u003e\u003cp\u003eThe meta-analysis revealed that the HR for MACE in North America was significantly higher compared to the overall HR in the study populations. In contrast, in many cases the HR was significantly lower than the general study risk ratio in other geographic regions, dragging the overall result to a significant decrease in MACE.\u003c/p\u003e\u003cp\u003eTwo SGLT2 studies with a significant decrease in MACE (SCORED\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and CREDENCE\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e) did not report the geographic distribution regarding MACE and thus were excluded from the meta-analysis. The primary outcome examined in these studies were cardiovascular mortality and hospitalizations/urgent visits due to heart failure in SCORED, and cardiovascular or renal mortality and measures of renal deterioration in CREDENCE. Even with regard to these results, for which a very significant decrease in risk was demonstrated by the treatment (HR of 0.74 and 0.7 respectively), the hazard reduction was not demonstrated in North America (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eSimilarly, the recent FLOW trial with the GLP-1 agonist Semaglutide demonstrates improved MACE and even total and CV mortality\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the primary cardiorenal outcome that improved even more (24% reduction) did not improve at all in North America (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCardiovascular diseases are the leading causes of morbidity and mortality in individuals with type 2 DM\u003csup\u003e35\u003c/sup\u003e. Thus, it is very important to prevent these complications in these patients. Most of the anti-hyperglycemic drugs have not proven effective in preventing CV diseases. The FDA requires CV outcome studies on any new DM medication before registration. The purpose is to prove safety, i.e., that the drug does not increase the incidence of CV complications. Therefore, many large controlled CV outcome studies have been conducted in recent years. All the studies done with GLP-1 agonists and SGLT2 inhibitors actually demonstrated good CV safety, i.e., these drugs do not increase the risk of CV diseases in patients with DM. A significant reduction in MACE was demonstrated in some studies with GLP-1 agonists and in a minority of studies conducted with SGLT2 inhibitors. Based on these studies, the FDA added the indication of CV disease prevention in DM patients to the registries of these drugs.\u003c/p\u003e\n\u003cp\u003eHowever, we have noticed a consistent pattern of geographical differences in the CV outcomes in these studies. We therefore aimed to methodologically examine all CV outcome studies that demonstrated an improvement in CV outcomes (defined as MACE) with these drugs for the regional effects in said outcomes. We found five such studies for GLP-1 agonists and two for SGLT2 inhibitors in which the geographic distribution of the MACE results were published.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found that our initial hypothesis for regional differences in the CV effects of GLP-1 agonists and SGLT2 inhibitors has been substantiated. Consistently, the results in North America were different from the overall result of the studies examined. No significant reduction in the incidence of MACE was demonstrated in North America. The overall positive effect on CV outcomes were dominated by the significant decrease in MACE incidence in other parts of the world. This phenomenon has been noticed before, for example, by Kang et al.\u003csup\u003e36\u003c/sup\u003e. It led them to conclude that GLP-1 agonists are more effective for CV prevention in the Asian population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith regard to SGLT2 inhibitors, we should mention that most of the CVOTs do not show a decrease in MACE. Our study only tested this with respect to the studies that demonstrated an improvement. Furthermore, in most of the CVOTs done both with GLP-1 agonists and SGLT2 inhibitors, CV mortality and all-cause mortality did not improve with treatment (5 of 9 for GLP-1 and 9 of 12 for SGLT2). Even in the study with the most impressive mortality reduction, EMPA-REG\u003csup\u003e8\u003c/sup\u003e (38% reduction in CV mortality), it did not occur in North America (Table 1E).\u003c/p\u003e\n\u003cp\u003eGeographic variation in large clinical trials is a known phenomenon. There are different characteristic in different populations that might affect the study outcomes. For example, geographic variations were reported in the TECOS\u003csup\u003e37\u003c/sup\u003e and EXSCEL\u003csup\u003e38\u003c/sup\u003e trials. The NAVIGATOR trial\u003csup\u003e39\u003c/sup\u003e examined the effect of Nateglinide and Valsartan in patients with impaired glucose tolerance. Although major regional differences were found regarding CV outcomes, the response to the medical treatment did not differ by geographic areas. By contrast, in the trials we analyzed in our study, different responses to medical treatment were found between different geographic areas. These variances were found for GLP-1 agonists as well as SGLT2 inhibitors, two classes of medication with completely different mechanisms of action. The different response to treatment was not in correlation with ethnic origin\u003csup\u003e5, 11\u003c/sup\u003e, ruling out ethnicity as a possible explanation. Furthermore, the “North American phenomenon” can be found with other study populations and other medications as well. For example, it was found in the SELECT study\u003csup\u003e40\u003c/sup\u003e, a study with the GLP-1 agonist Semaglutide in obese patients without DM (Table 1F), as well as in FIGARO-DKD\u003csup\u003e41\u003c/sup\u003e and FINEARTS-HF\u003csup\u003e42\u003c/sup\u003e, trials conducted with Finerenone, a mineralocorticoid receptor antagonist (Table 1G).\u003c/p\u003e\n\u003cp\u003eRegional differences in baseline treatment might possibly influence CV outcomes. For example, the fewer preventive approaches applied in a region, the more effective these drugs might be. This option requires further investigation, as no data was clearly provided in the studies. In fact, we could not identify any study that reported a difference in baseline treatment by geographic region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe concern of the applicability of data with regional differences on decisions for the North American population was already raised by Dr. Budnitz when he voted against Liraglutide approval for CV prevention in 2017\u003csup\u003e6\u003c/sup\u003e. He then had only the results of one trial. The FDA requires that the effectiveness of the drugs it approves be investigated in the target population of said drugs. Indeed, participants from the North American region, the primary target population for FDA-approved drugs, took part in all the trials included in our analysis. However, their CV risk did not improve with treatment. Clearly, none of the studies demonstrated significant improvement in MACE with treatment in the North American region.\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eThe main limitation of our study is that we cannot be sure that the size of the groups in North America is large enough to be powered to show independent superiority. However, this limitation does not prevent our conclusion, as:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. The North American HR is clearly and consistently higher and different from the total HR;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Smaller groups in other geographical areas reach statistical significance and their HRs are lower than the total HRs;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Even if the North American groups are too small to demonstrate MACE improvement by themselves, we can at least declare that prevention of CV disease (as reflected by MACE) by these medications has not been proven in North America. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eEven in those trials in DM patients with GLP-1 agonist and SGLT2 inhibitors that demonstrate significant improvement in MACE incidence, said improvement did not occur in North America. As North America is the primary target population for the FDA, it should reevaluate and reconsider the registration of the indication \u0026ldquo;Disorder of CV System: Prophylaxis \u0026ndash; Type 2 DM\u0026rdquo; for these medications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was given to this study.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thanks Mr. David Dvash for his valuable help in preparing the manuscript.\u003c/p\u003e\n\u003cp\u003eConflict of interests: The author declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eIsmail-Beigi F, Craven T, Banerji MA, et al. Effect of intensive treatment of hyperglycaemia on microvascular outcomes in type 2 diabetes: an analysis of the ACCORD randomised trial. 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International Variation in Outcomes Among People with Cardiovascular Disease or Cardiovascular Risk Factors and Impaired Glucose Tolerance: Insights from the NAVIGATOR Trial. J Am Heart Assoc. 2017; \u003cstrong\u003e6\u003c/strong\u003e: e003892.\u003c/li\u003e\n \u003cli\u003eLincoff AM, Brown-Frandsen K, Colhoun HM, et al. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N Engl J Med. 2023; \u003cstrong\u003e389\u003c/strong\u003e: 2221-2232.\u003c/li\u003e\n \u003cli\u003ePitt B, Filippatos G, Agarwal R, et al. Cardiovascular Events with Finerenone in Kidney Disease and Type 2 Diabetes. N Engl J Med. 2021; \u003cstrong\u003e385\u003c/strong\u003e: 2252-2263.\u003c/li\u003e\n \u003cli\u003eSolomon SD, McMurray JJV, Vaduganathan M, et al. Finerenone in Heart Failure with Mildly Reduced or Preserved Ejection Fraction. N Engl J Med. 2024; \u003cstrong\u003e391\u003c/strong\u003e: 1475-1485.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-6863965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6863965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on cardiovascular outcome trials, the FDA has approved the indication to reduce the risk of cardiovascular events in patients with diabetes for GLP-1 receptor agonists and SGLT-2 inhibitors. We assess the effect of GLP-1 receptor agonists and SGLT-2 inhibitors on cardiovascular risk reduction according to geographic areas. We performed a systematic search of cardiovascular outcome trials in PubMed (MEDLINE) until September 2024. We included cardiovascular outcome trials with these medications that demonstrated a reduction in major cardiovascular events in patients with diabetes and provided geographic sub-analyses of these outcomes. The data was extracted from the study publications, including their supplementary material. A statistical meta-analysis was conducted to assess differences in cardiovascular risk reduction by region. The primary outcome was the regional variation in cardiovascular risk reduction, as measured by major cardiovascular events, associated with these medications. We found 5 studies with GLP-1 receptor agonists and 2 with SGLT-2 inhibitors in which a reduction in major cardiovascular events was recorded and related geographic sub-analysis was published. None of the studies demonstrated major cardiovascular events improvement in North America. Almost all the studies found higher hazard ratio for major cardiovascular events in North America as compared to whole study hazard ratio. We conclude that GLP-1 receptor agonists and SGLT-2 inhibitors do not decrease cardiovascular risk in diabetic patients in North America. Based on our results, the FDA should reconsider the registered indication of cardiovascular prevention in patients with diabetes mellitus for these medications.\u003c/p\u003e","manuscriptTitle":"Geographic Variation in Cardiovascular Disease Prevention by GLP-1 Receptor Agonists and SGLT2 Inhibitors: A Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 08:02:17","doi":"10.21203/rs.3.rs-6863965/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":"846ea417-f855-447c-b4c0-7aea0a3ec9de","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52440227,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"},{"id":52440228,"name":"Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases"}],"tags":[],"updatedAt":"2025-08-08T12:21:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 08:02:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6863965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6863965","identity":"rs-6863965","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}