A Systematic Review and Meta-analysis of the effect of Hyperglycemia on Admission for Acute Myocardial Infarction in Diabetic and non-Diabetic patients

A Systematic Review and Meta-analysis of the effect of Hyperglycemia on Admission for Acute Myocardial Infarction in Diabetic and non-Diabetic patients | 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 A Systematic Review and Meta-analysis of the effect of Hyperglycemia on Admission for Acute Myocardial Infarction in Diabetic and non-Diabetic patients Reem Alawaji, Mohammed Musslem, Emtenan Alshalahi, Abdaluziz Alanzan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4563999/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Sep, 2024 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted 20 You are reading this latest preprint version Abstract Introduction: Regarding a potential relationship between diabetes and the prognostic significance of hyperglycemia in patients presenting with acute myocardial infarction (AMI), there is still debate. Therefore, we aimed in this study to demonstrate the effect of hyperglycemia on different outcomes in AMI patients whether they are diabetic or not. Methods Using the following search strategy: “Diabetes” or “Diabetic” AND “Acute myocardial infarction” OR “AMI” AND “hyperglycemia” OR “glucose level”, we searched PubMed, Web of Science, and Scopus for eligible articles that should undergo the screening process to determine its ability to be included in our study. Using Review Manager version 5.4 software, we conducted the meta-analysis of the included studies by pooling the mean difference in continuous variables, number and total of dichotomous variables to measure the odds ratio (OR), and generic inverse variance of OR or hazard ratio (HR) as they were reported in the included studies. Results The difference between the diabetes and non-diabetes patients regarding blood glucose level was found to be statistically significant with SMD of 1.39 (95%CI: 1.12, 1.66, p < 0.00001). Hyperglycemia in diabetic patients was statistically significant associated with mortality with HR of 1.92 (95%CI: 1.45, 2.55, p < 0.00001) and OR of 1.76 (95%CI: 1.15, 2.7, p = 0.01). In non-diabetic patients admitted with AMI, hyperglycemia was statistically significant associated with mortality with HR of 1.56 (95%CI: 1.31, 1.86, p < 0.00001), and OR of 2.89 (95%CI: 2.47, 3.39, p < 0.00001). Moreover, hyperglycemia in diabetic patients admitted with AMI was statistically significant associated with occurrence of MACE with HR of 1.9 (95%CI: 1.19, 3.03, p = 0.007) and hyperglycemia in non-diabetic AMI patients was statistically significant associated with occurrence of MACE with HR of 1.6 (95%CI: 1.15, 2.23, p = 0.006). Conclusion Hyperglycemia in AMI patients is a predictor of worse outcomes including MACE, and mortality whether these patients are diabetic or not. Some factors act as predictors for mortality in these patients including older age, higher glucose levels on admission, and high Killip class. Diabetes acute myocardial infarction glucose hyperglycemia Figures Figure 1 Figure 2 Figure 3 Introduction Globally, acute coronary syndromes (ACS) constitute a major cause of mortality. The short- and long-term death rates among patients who report with acute myocardial infarction (AMI) are concerning, despite being on the decline ( 1 ). According to predictions from the American Heart Association, there will be 19 million cardiovascular deaths globally in 2020, up 18.7% from 2010. As a result, the direct burden of ACS has significantly increased in the US ( 1 ). AMI is the most severe form of ACS; nevertheless, reperfusion treatment has lately led to a drop in AMI-related mortality ( 2 ). Even in the absence of preexisting diabetes, hyperglycemia can emerge during an AMI due to rising levels of catecholamines, steroids, glucagon, and falling insulin levels brought on by stress ( 3 ). Twenty to fifty percent of patients with ST-segment elevation myocardial infarction (STEMI) had stress hyperglycemia at the time of admission, according to previous research ( 4 , 5 ). Though the precise definition of stress hyperglycemia in relation to an AMI is unknown, it has been defined as momentarily raising plasma glucose levels in critically sick patients who have not previously been diagnosed with diabetes mellitus ( 4 – 6 ). A major factor in the instability and rupture of atherosclerotic plaques, hyperglycemia also acts as a catalyst for endothelial dysfunction and hyperinflammation (by the regulation of microRNA and apoptotic pathways, among other mechanisms) ( 7 ). For example, Sardu et al. ( 8 ) observed that stress hyperglycemia during STEMI may impact the composition of coronary thrombus, leading to enhanced inflammation as seen by raised levels of tumor necrosis factor-α in coronary thrombi taken from the hyperglycemic patients. Admission glucose, or glucose concentrations at hospital admission, has been the subject of several studies that have examined the relationship between stress hyperglycemia and both short- and long-term mortality in patients with AMI who have or do not have diabetes ( 9 – 12 ). Interestingly, some of these studies have found a stronger association in patients without diabetes ( 13 , 14 ). Hyperglycemia during the event seems to generally increase major adverse cardiovascular events (MACE) (including re-hospitalization for heart failure, stroke, and coronary disease) in addition to mortality ( 15 ). According to several epidemiological research, the prevalence of hyperglycemia in patients hospitalized for ACS varies from 3–71% ( 16 ). Whether thrombolysis or primary percutaneous coronary intervention (pPCI) is used as a reperfusion therapy, hyperglycemia at the time of admission has been found to be a significant predictor of unfavorable outcomes in patients with AMI ( 17 , 18 ). Regarding a potential relationship between diabetes and the prognostic significance of hyperglycemia in patients presenting with AMI, there is still debate. Therefore, we aimed in this study to demonstrate the effect of hyperglycemia on different outcomes in AMI patients whether they are diabetic or not. Methods Adhering to the Cochrane Handbook of Systematic Reviews of Interventions at each step (19), and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement's guidelines, we conducted this systematic review and meta-analysis (20). Database searching Using the following search strategy: “Diabetes” or “Diabetic” AND “Acute myocardial infarction” OR “AMI” AND “hyperglycemia” OR “glucose level”, we searched PubMed, Web of Science, and Scopus for eligible articles that should undergo the screening process to determine its ability to be included in our study. Screening After database searching, we removed the duplicates from the resulting articles using EndNote version 7 (21). software, then we uploaded the remaining articles on Rayyan software (22) to conduct the process of screening. First, four authors who worked independently conducted the screening by title and abstract to see the eligibility for inclusion, and then they conducted full-text screening of the included articles from the previous step. Any conflicts were referred to a senior author to resolve. Inclusion and exclusion criteria The predetermined inclusion and exclusion criteria used for screening were any observational (cohort, cross-sectional, or case-control) and randomized controlled trials (RCT) investigating the effects of hyperglycemia in diabetic or non-diabetic AMI patients on short or long-term outcomes such as mortality and occurrence of MACE. We excluded studies that didn’t measure the effect of hyperglycemia, populations other than AMI, and case reports, case series and reviews. Quality assessment For the included observational cohort studies, we used the New Castle Ottawa scale tool provided by Cochrane for the assessment of quality. It is composed of 8 questions with a maximum of one star for each except for the comparability question that can get two stars. Therefore, the highest score is nine while the lowest score is zero. Studies scoring from 0 to 3 were considered of low quality, 4-6 were of moderate quality, and 7-9 were of high quality (23). Data extraction Using Microsoft Excel sheets, four independent authors conducted the process of data extraction to extract the baseline data (study design, country, sample size, groups, age, and gender) in addition to the outcomes (blood glucose on admission, odds ratio [OR], hazard ratio [HR] of mortality and MACE, mortality rate, factors affecting mortality including age, admission glucose levels, and Killip class) of the included studies. Any differences or conflicts were resolved by a senior author. Statistical analysis Using Review Manager version 5.4 software, we conducted the meta-analysis of the included studies by pooling the mean difference in continuous variables, number and total of dichotomous variables to measure the OR, and generic inverse variance of OR or HR as they were reported in the included studies. The results were considered statistically significant at p-value of less than or equal to 0.05. The used confidence intervals (CI) were 95%, and the I 2 was used for testing the heterogeneity with the p-value for significance. Sensitivity analysis Using OpenMetaAnalyst software, we conducted sensitivity analysis using leave-one-out method to remove the studies that caused heterogeneity in the heterogeneous outcomes. Results Database searching and screening. The database searching process yielded a total of 2157 articles with 761 duplicates so a total of 1396 articles entered the title and abstract screening. A total of 1369 articles were excluded and then 27 articles were screened by full-text to yield a total of 19 articles ( 24 – 42 ) for the meta-analysis. (Fig. 1 ) Quality assessment According to NOS, 13 studies were considered of high quality while only six studies were considered of moderate quality. (Table 1 ) Table 1 Quality assessment of the included cohort studies using New Caste Ottawa Scale Study name Representativeness of the exposed cohort (★) Selection of the non exposed cohort (★) Ascertainment of exposure (★) Demonstration that outcome of interest was not present at start of study (★) Comparability of cohorts on the basis of the design or analysis (max★★) Assessment of outcome (★) Was follow-up long enough for outcomes to occur? (★) Adequacy of follow up of cohorts (★) Quality level Upur 2022 ★ 0 ★ ★ ★★ ★ ★ 0 High ( 7 ) Paolisso 2021 ★ ★ 0 ★ ★★ ★ ★ 0 High ( 7 ) Ritsinger 2021 0 0 ★ 0 ★★ ★ ★ ★ Moderate ( 6 ) Ding 2019 ★ ★ ★ 0 ★★ ★ ★ ★ High ( 8 ) Schmitz 2022 0 0 ★ ★ ★★ ★ ★ 0 Moderate ( 6 ) Cui 2022 ★ ★ ★ ★ ★★ ★ ★ ★ High ( 9 ) Kojima 2019 ★ ★ ★ 0 ★★ ★ ★ ★ High ( 8 ) Thoegersen 2020 ★ ★ ★ ★ ★★ ★ 0 ★ High ( 8 ) Demarchi 2020 ★ ★ 0 ★ ★ ★ ★ 0 Moderate ( 6 ) Ritsinger 2019 ★ ★ 0 0 ★★ 0 ★ ★ Moderate ( 6 ) Mamadjanov 2021 ★ ★ 0 ★ ★ ★ ★ 0 Moderate ( 6 ) Cui 2021 ★ ★ 0 ★ ★ ★ ★ 0 Moderate ( 6 ) Zhou 2020 ★ ★ 0 0 ★★ ★ ★ ★ High ( 7 ) Ferreira 2021 ★ ★ ★ ★ ★★ 0 0 ★ High ( 7 ) Jomaa 2018 ★ ★ ★ ★ ★★ ★ 0 0 High ( 7 ) Chattopadhyay 2018 ★ ★ ★ ★ ★★ ★ ★ 0 High ( 8 ) Chattopadhyay 2019 ★ ★ ★ ★ ★★ 0 ★ ★ High ( 8 ) Yuan 2022 ★ ★ ★ ★ ★★ ★ 0 0 High ( 7 ) Cui 2023 ★ ★ ★ ★ ★★ 0 ★ 0 High ( 7 ) Baseline characteristics All the included 19 articles were cohort studies conducted in different countries including Japan, Germany, China, United Kingdom, and others. Most of the studies compared hyperglycemic AMI patients in diabetes and non-diabetes. Other studies compared either of the two mentioned groups against patients with no hyperglycemia and no diabetes. The mean age of the participants ranged from 56.3 years old to 72.3 years old. (Table 2 ) Table 2 Summary and baseline characteristics of the included studies Study ID Design Country Group 1 Group 2 Sample size Age Male, n(%) Group 1 Group 2 Group 1 Group 2 Group 1 Group 2 Upur 2022 Cohort China Hyperglycemia and no diabetes Hyperglycemia and diabetes 1179 425 56.3 (12.3) 965 (82) 279 ( 66 ) Paolisso 2021 Cohort Italy MIOCA with hyperglycemia MINOCA with hyperglycemia 877 38 72.3 (13.4) 74 (10.8) 615 ( 70 ) 12 (31.6) Ritsinger 2021 Cohort Sweden Hyperglycemia and no diabetes No hyperglycemia or diabetes 10094 103 70 (23.7) 64.7 (27.1) 6927 (68.6 76 (73.8) Ding 2019 Cohort China No hyperglycemia or diabetes Hyperglycemia and no diabetes 1216 112 64.8 (15.6) 65.3 ( 12 ) 930 (76.5) 86 (76.8) Schmitz 2022 Cohort Germany Hyperglycemia and diabetes Hyperglycemia and no diabetes 681 1630 68 ( 11 ) 63.7 (12.7) 481 (70.6) 1203 (73.8) Cui 2022 Cohort China Hyperglycemia and diabetes Hyperglycemia and no diabetes 1681 1006 62.9 ( 12 ) 61.4 (13.1) 1174 (69.8) 767 (76.2) Kojima 2019 Cohort Japan Hyperglycemia and no diabetes Hyperglycemia and diabetes 970 600 67.6 (12.6) 67.4 (9.7) 703 (72.5) 433 (72.3) Thoegersen 2020 Cohort Denmark Hyperglycemia and no diabetes Hyperglycemia and diabetes 1307 273 66.9 (12.20) 69.19 (9.95) 982 (75.1) 198 (72.5) Demarchi 2020 Cohort Italy Hyperglycemia and no diabetes Hyperglycemia and diabetes 2958 65.6 (12.6) 2248 (76) Ritsinger 2019 Cohort Sweden Hyperglycemia and no diabetes Hyperglycemia and diabetes 198 1124 67 ( 11 ) NR Mamadjanov 2021 Cohort Germany Hyperglycemia and no diabetes Hyperglycemia and diabetes 3434 2096 65–84 3514 (63.5) Cui 2021 Cohort China Hyperglycemia and no diabetes Hyperglycemia and diabetes 425 239 67.7 (13.2) 70.3 (11.3) 300 (70.6) 127 (53.3) Zhou 2020 Cohort China Hyperglycemia and no diabetes Hyperglycemia and diabetes 60 95 57.1 (10.6) 57.1 (11.5) 48 (80) 77 (81.1) Ferreira 2021 Cohort Portugal Hyperglycemia and no diabetes Hyperglycemia and diabetes 426 325 69.9 (12.9) 68.5 (11.2) 291 (68.5) 220 (67.7) Jomaa 2018 Cohort Tunisia Hyperglycemia and no diabetes Hyperglycemia and diabetes 865 464 60.39 (12.8) 997 (77.3) Chattopadhyay 2018 Cohort United Kingdom Hyperglycemia and no diabetes No hyperglycemia or diabetes 172 165 69 ( 20 ) 61 ( 13 ) 122 (70.9) 120 (72.7) Chattopadhyay 2019 Cohort United Kingdom Hyperglycemia and no diabetes No hyperglycemia or diabetes 200 474 68 (14.6) 64 (12.6) 136 (68.0) 346 (73.0) Yuan 2022 Cohort China Hyperglycemia and no diabetes No hyperglycemia or diabetes 147 1856 65.6 (18.3) 64.7 (16.3) 109 (74.1) 1466 (79) Cui 2023 Cohort China Hyperglycemia and no diabetes Hyperglycemia and diabetes 3227 2081 61.3 (12.9) 63.1 (11.5) 2541 (78.7) 1420 (68.2) Meta-analysis The difference between the diabetes and non-diabetes patients regarding blood glucose level was found to be statistically significant with SMD of 1.39 (95%CI: 1.12, 1.66, p < 0.00001) with heterogeneity (I 2 = 98%, p < 0.00001). (Fig. 2 ) Hyperglycemia in diabetes patients was found to be more statistically significant associated with mortality compared to hyperglycemia in non-diabetic patients with OR of 1.47 (95%CI: 1.08, 1.99, p = 0.01) and heterogeneity measured by I 2 = 73%, p = 0.005. (Fig. 3 ) Hyperglycemia in diabetic patients was statistically significant associated with mortality with HR of 1.92 (95%CI: 1.45, 2.55, p < 0.00001) with heterogeneity (I 2 = 81%, p < 0.0001). and OR of 1.76 (95%CI: 1.15, 2.7, p = 0.01) with heterogeneity (I 2 = 78%, p = 0.01). In non-diabetic patients admitted with AMI, hyperglycemia was statistically significant associated with mortality with HR of 1.56 (95%CI: 1.31, 1.86, p < 0.00001), heterogeneity (I 2 = 72%, p = 0.002) and OR of 2.89 (95%CI: 2.47, 3.39, p < 0.00001) and no heterogeneity (I 2 = 0%). Moreover, hyperglycemia in diabetic patients admitted with AMI was statistically significant associated with occurrence of MACE with HR of 1.9 (95%CI: 1.19, 3.03, p = 0.007) and heterogeneity (I 2 = 83%, p = 0.003). In addition, hyperglycemia in non-diabetic AMI patients was statistically significant associated with occurrence of MACE with HR of 1.6 (95%CI: 1.15, 2.23, p = 0.006) and heterogeneity (I 2 = 89%, p < 0.00001). Age was among the factors that predicted mortality after hyperglycemia in diabetic and non-diabetic AMI patients with HR of 1.05 (1.04, 1.07, p < 0.00001), and no heterogeneity (I 2 = 0%) in diabetic patients and HR of 1.07 (95%CI: 1.02, 1.12, p = 0.01) and heterogeneity (I 2 = 84%, p = 0.01) in non-diabetic patients. It was observed that increased glucose levels on admission are statistically significant predictors of mortality in diabetic and non-diabetic patients with OR of 4.7 (95%CI: 1.48, 14.91, p = 0.009) and no heterogeneity (I 2 = 0%) and OR of 1.88 (95%CI: 1.52, 2.33, p < 0.00001) and no heterogeneity (I 2 = 0%), respectively. Killip class ≥ 2 was statistically significant associated with mortality in non-diabetic AMI patients admitted with hyperglycemia with HR of 1.9 (95%CI: 1.4, 2.57, p < 0.0001) and non-significant heterogeneity (I 2 = 42%, p = 0.18), while no significant association was observed between Killip class and mortality in diabetic patients with HR of 0.94 (95%CI: 0.62, 1.42, p = 0.76). Sensitivity analysis After conducting leave-one-out analysis for blood glucose levels comparison among the diabetic and non-diabetic patients, it was found that Cui et al. (2022) ( 39 ), and Kojima et al. (2019) ( 38 ) were the main sources of heterogeneity. For the comparison between the diabetic and non-diabetic patients regarding mortality, it was observed that Schmitz et al. (2022) ( 28 ) was the main source of heterogeneity. For the mortality outcome in diabetic patients using HR, Cui et al. (2021) ( 26 ), and Kojima et al. (2019) ( 38 ) were considered the main reasons for heterogeneity. While using OR, Cui et al. (2023) ( 24 ) was the main source of heterogeneity. For the mortality outcome in non-diabetic patients using HR, Cui et al. (2021) ( 26 ) was considered the main source for heterogeneity. Regarding the occurrence of MACE in diabetic patients, Cui et al. (2022) ( 39 ) caused the heterogeneity in the outcome, while Ristinger et al. (2021) ( 41 ) caused the heterogeneity in the MACE outcome of non-diabetics. Yuan et al. (2022) ( 25 ) was observed to be the main cause of heterogeneity in the association of age with mortality using OR. Discussion The current study showed that compared hyperglycemia was more evident in diabetic patients on their admission with AMI compared to non-diabetic patients and this is due to their already present hyperglycemia due to diabetes which increases during AMI. We demonstrated that hyperglycemia in AMI was significantly associated with increased mortality whatever the diabetes status was. However, diabetic patients were more likely to have increased mortality rates compared to non-diabetic patients. Moreover, some factors were associated with increased mortality in AMI patients admitted with hyperglycemia such as older age, admission glucose, and Killip class. It is clear from the present results that by increasing glucose levels, the mortality rates and MACE will increase whether the patient is diabetic or not. By upregulating gluconeogenesis and glycogenolysis, the rising levels of glucagon, cortisol, and cytokines in the context of AMI facilitate the synthesis of glucose ( 43 – 45 ). Stress hyperglycemia is a common condition because decreased insulin production by pancreatic β-cells was unable to counteract the hyperglycemic effects of these counter-regulatory hormones and cytokines ( 46 , 47 ). Even worse, by releasing free fatty acids from adipose tissue and inducing serine/threonine kinases that disrupt insulin signaling, the sympathetic nervous system's activation causes insulin resistance ( 48 , 49 ). Stress hyperglycemia generally causes prothrombosis, oxidative stress, inflammation, endothelial dysfunction, reduction in coronary flow, enlargement of infarct size, and compromised heart function. A study involving 460 patients diagnosed with STEMI, for instance, revealed that individuals with hyperglycemia had a lower incidence of Thrombolysis In Myocardial Infarction (TIMI) flow grade 3 prior to initial PCI (12% vs. 28%, P < 0.001) ( 50 ). Improving glucose levels enhances hospitalized patients' clinical results. The ideal course of treatment for hyperglycemia is debatable, though. The American College of Endocrinology suggested in 2009 that blood glucose levels in critically ill hospitalized patients be kept at 110 mg/d, citing research by Leuven as support ( 51 ). The American Diabetes Association (ADA) advised in 2020 that critically sick patients with blood glucose levels higher than 180 mg/dL be given insulin therapy, with a target blood glucose range of 140–180 mg/dL ( 52 ). An upper glucose limit of 180 mg/dL is recommended by a prior study for critically sick individuals who do not have diabetes ( 53 ). Furthermore, when determining the target glucose range, comorbidities need to be taken into account. There is currently no set cutoff value that can be used effectively, despite the fact that mounting data indicates that hyperglycemia has a poor prognosis in AMI patients regardless of whether they have diabetes. A blood glucose level more than 7.78 mmol/L was considered acute hyperglycemia in the study by Khalfallah et al. ( 54 ), however patients in the study by Li et al. ( 55 ) were categorized based on a threshold of 11.11 mmol/L. The term "admission hyperglycemia" was not defined consistently in these studies, and it should not be confused with diabetes mellitus, impaired fasting blood glucose (FBG), or aberrant glucose tolerance. More importantly, since patients with and without diabetes have distinct basal blood glucose concentrations and varying degrees of acute rise during stress, there is no justification for utilizing the same cut-off value for both groups. Suleiman et al. ( 56 ) discovered that fasting and immediate blood glucose levels were independent predictors of the prognosis for AMI; however, fasting hyperglycemia had a stronger predictive value than immediate hyperglycemia. In order to prevent diabetes from being confused with undiagnosed diabetes, Cui et al. ( 26 ) used the blood glucose level as the FBG at admission and the diagnosis of diabetes at the time of discharge. As a result, our approach of predicting blood glucose levels to predict the clinical prognosis of AMI patients was more logical and scientific. The prognostic significance of admission hyperglycemia in individuals with and without diabetes has been the main focus of previous research ( 32 , 42 , 57 , 58 ). According to certain research, entry blood glucose was not a more reliable or strong independent predictor of risk than persistent hyperglycemia. Hyperglycemia, defined as blood glucose levels ≥ 8.9 mmol/L, is linked to pre-discharge left ventricular dysfunction and reduced myocardial perfusion even in cases where the infarct-related artery is still open 24 hours after the onset of symptoms ( 59 ). According to a different study, high glucose at admission is not as strongly correlated with 30-day MACE as persistent hyperglycemia following myocardial infarction ( 60 ). In a prior study, FBG outperformed admission glucose in terms of 30-day mortality ( 56 ). The advantage of FBG over random glucose levels in predicting the outcome is likely due to various factors, including variations in caloric intake and duration since the last meal. FBG was an independent risk factor for the Gensini score in AMI patients, and random blood glucose and FBG were positively linked with the Gensini score in patients with AMI in a prior study ( 61 ). These results show that rather than an underlying "diabetic state," rapid and persistent rise of blood glucose may cause microvascular dysfunction and worsen outcomes. Greater entry glucose concentrations were associated with higher mortality in 1219 non-diabetic individuals following AMI, but larger glucose decreases during a 24-hour period were associated with decreased 30-day mortality. Both the baseline glucose and the 24-hour glucose change were still highly significant indicators of death after 180 days ( 62 ). As a result, it was thought that FBG levels at admission might be a better indicator of the persistence of blood glucose rise and a stronger prospective predictor of clinical outcomes in patients with AMI. There is no precise blood glucose cutoff value for AMI patients to anticipate adverse events, nor is there a precise definition for admission hyperglycemia in AMI patients. Hemoglobin A1c (HbA1c), the glucose-HbA1c ratio (GHR), and the stress hyperglycemia ratio (SHR) have all been linked to favorable clinical outcomes in AMI and other disease processes. However, because thresholds have been chosen at random, their ideal values remain unclear ( 63 , 64 ). Cui et al. ( 26 ) demonstrated that admission glucose had a poor predictive power for long-term mortality in patients with and without diabetes, with area under the ROC curve values of just 0.634 and 0.660, respectively. This finding may have been impacted by the small sample size. On the other hand, the study's research methodology and research questions may offer useful information for next investigations. According to a recent study by Hao et al. ( 65 ), entry hyperglycemia may be a reliable indicator of an AMI patient's outcome. The difference in predictive value between the patient groups with and without diabetes was not examined in the study, though. In patients with AMI, admission hyperglycemia had a predictive value for hospital mortality, according to another study9, although the predicted value varied depending on whether the patient had diabetes or not. Cui et al. ( 26 ) conducted a long-term follow-up and made a distinction between patients with and without diabetes, in contrast to the aforementioned analysis. The findings demonstrated that admission hyperglycemia did, in fact, have a different effect on patients with and without diabetes, since it was significantly linked to a worse long-term prognosis in the former group while having no effect on the latter. One rationale is that the presence of AMI in patients with diabetes may disguise the effects of admission hyperglycemia, which in turn may lead to a poor long-term prognosis ( 66 , 67 ). Admission hyperglycemia is a sign of serious myocardial infarction and cardiac dysfunction in AMI patients without diabetes ( 68 , 69 ). individuals without diabetes may have higher blood glucose due to greater ischemia or hemodynamic damage than individuals with diabetes, whose blood glucose levels are generally elevated ( 69 ). Another explanation is that because the hyperglycemia group of AMI patients without diabetes tends to have greater levels of brain natriuretic polypeptide and much higher C-reactive protein, they suffered a more widespread myocardial infarction. The third theory is that insulin treatment for diabetes patients was more common. Appropriate insulin therapy can improve cardiac perfusion, according to studies. It has been demonstrated that insulin increases the synthesis of nitric oxide by activating endothelial nitric oxide synthase ( 70 ). Furthermore, insulin may reduce cardiac cell apoptosis in ischemia patients, among other positive effects ( 71 ). This study has some limitations including that all of the included studies are of observational design which has higher risk of bias than RCTs. There is no common definition for hyperglycemia and no common cut off value that are relied one. Therefore, further RCTs are required in addition to test accuracy of the cut off value that should be used for prognosis whether in diabetic or non-diabetic patients. Conclusion Hyperglycemia in AMI patients is a predictor of worse outcomes including MACE, and mortality whether these patients are diabetic or not. Some factors act as predictors for mortality in these patients including older age, higher glucose levels on admission, and high Killip class. However, further studies are required to put a definite value for hyperglycemia and cut off for prognosis. Declarations Ethical Considerations Source of Data: This study involved the collection and analysis of data from previously published studies. No new human or animal subjects were involved directly in this research. All included studies were required to have obtained appropriate ethical approval and informed consent from participants as indicated in their respective publications. Consent to Participate and Publish: Since this research is a secondary analysis of existing data, direct consent to participate and publish was not required from individual subjects. However, the original studies included in this review and meta-analysis documented their consent procedures, ensuring compliance with ethical standards. Data Integrity and Confidentiality: Efforts were made to ensure the integrity and confidentiality of data by including only publicly available and anonymized data from published studies. Any sensitive information from the original studies was handled in accordance with ethical guidelines to protect the privacy of individuals. Guidelines Followed Guidelines Followed: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines For any further information or clarifications, please contact Alawaji, Reem ( [email protected] ) Funding Statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. All expenses related to the study were covered by the authors themselves. Author Contribution All authors have contributed significantly to the study and manuscript preparation, and all authors are in agreement with the content of the manuscript. Data Availability Data is provided within the manuscript References Steg PG, James SK, Atar D, Badano LP, Blömstrom-Lundqvist C, Borger MA, et al. ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J. 2012;33(20):2569–619. Buntaine AJ, Shah B, Lorin JD, Sedlis SP. 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Chronic tumor necrosis factor-alpha treatment causes insulin resistance via insulin receptor substrate-1 serine phosphorylation and suppressor of cytokine signaling-3 induction in 3T3-L1 adipocytes. Endocrinology. 2007;148(6):2994–3003. Dünser MW, Hasibeder WR. Sympathetic overstimulation during critical illness: adverse effects of adrenergic stress. J Intensive Care Med. 2009;24(5):293–316. Bartnik M, Malmberg K, Hamsten A, Efendic S, Norhammar A, Silveira A, et al. Abnormal glucose tolerance–a common risk factor in patients with acute myocardial infarction in comparison with population-based controls. J Intern Med. 2004;256(4):288–97. Wallander M, Bartnik M, Efendic S, Hamsten A, Malmberg K, Ohrvik J, et al. Beta cell dysfunction in patients with acute myocardial infarction but without previously known type 2 diabetes: a report from the GAMI study. Diabetologia. 2005;48(11):2229–35. Klein J, Fasshauer M, Ito M, Lowell BB, Benito M, Kahn CR. beta(3)-adrenergic stimulation differentially inhibits insulin signaling and decreases insulin-induced glucose uptake in brown adipocytes. J Biol Chem. 1999;274(49):34795–802. Kelley DE, Mokan M, Simoneau JA, Mandarino LJ. Interaction between glucose and free fatty acid metabolism in human skeletal muscle. J Clin Invest. 1993;92(1):91–8. Timmer JR, Ottervanger JP, de Boer MJ, Dambrink JH, Hoorntje JC, Gosselink AT, et al. Hyperglycemia is an important predictor of impaired coronary flow before reperfusion therapy in ST-segment elevation myocardial infarction. J Am Coll Cardiol. 2005;45(7):999–1002. Moghissi ES, Korytkowski MT, DiNardo M, Einhorn D, Hellman R, Hirsch IB, et al. American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Endocr Pract. 2009;15(4):353–69. 15. Diabetes Care in the Hospital: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S193–202. Ichai C, Preiser JC. International recommendations for glucose control in adult non diabetic critically ill patients. Crit Care. 2010;14(5):R166. Khalfallah M, Abdelmageed R, Elgendy E, Hafez YM. Incidence, predictors and outcomes of stress hyperglycemia in patients with ST elevation myocardial infarction undergoing primary percutaneous coronary intervention. Diab Vasc Dis Res. 2020;17(1):1479164119883983. Li Y, Li X, Zhang Y, Zhang L, Wu Q, Bai Z, et al. Impact of glycemic control status on patients with ST-segment elevation myocardial infarction undergoing percutaneous coronary intervention. BMC Cardiovasc Disord. 2020;20(1):36. Suleiman M, Hammerman H, Boulos M, Kapeliovich MR, Suleiman A, Agmon Y, et al. Fasting glucose is an important independent risk factor for 30-day mortality in patients with acute myocardial infarction: a prospective study. Circulation. 2005;111(6):754–60. Kim EJ, Jeong MH, Kim JH, Ahn TH, Seung KB, Oh DJ, et al. Clinical impact of admission hyperglycemia on in-hospital mortality in acute myocardial infarction patients. Int J Cardiol. 2017;236:9–15. Sia CH, Chan MH, Zheng H, Ko J, Ho AF, Chong J, et al. Optimal glucose, HbA1c, glucose-HbA1c ratio and stress-hyperglycaemia ratio cut-off values for predicting 1-year mortality in diabetic and non-diabetic acute myocardial infarction patients. Cardiovasc Diabetol. 2021;20(1):211. Kosuge M, Kimura K, Ishikawa T, Shimizi T, Hibi K, Toda N, et al. Persistent hyperglycemia is associated with left ventricular dysfunction in patients with acute myocardial infarction. Circ J. 2005;69(1):23–8. van der Horst IC, Nijsten MW, Vogelzang M, Zijlstra F. Persistent hyperglycemia is an independent predictor of outcome in acute myocardial infarction. Cardiovasc Diabetol. 2007;6:2. Qin X, Shao L, Zhang L, Ma L, Xiong S. Investigation of Interaction between Vitamin D Receptor Gene Polymorphisms and Environmental Factors in Early Childhood Caries in Chinese Children. Biomed Res Int. 2019;2019:4315839. Goyal A, Mahaffey KW, Garg J, Nicolau JC, Hochman JS, Weaver WD, et al. Prognostic significance of the change in glucose level in the first 24 h after acute myocardial infarction: results from the CARDINAL study. Eur Heart J. 2006;27(11):1289–97. Deedwania P, Kosiborod M, Barrett E, Ceriello A, Isley W, Mazzone T, et al. Hyperglycemia and acute coronary syndrome: a scientific statement from the American Heart Association Diabetes Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation. 2008;117(12):1610–9. Roberts GW, Quinn SJ, Valentine N, Alhawassi T, O'Dea H, Stranks SN, et al. Relative Hyperglycemia, a Marker of Critical Illness: Introducing the Stress Hyperglycemia Ratio. J Clin Endocrinol Metab. 2015;100(12):4490–7. Hao Y, Lu Q, Li T, Yang G, Hu P, Ma A. Admission hyperglycemia and adverse outcomes in diabetic and non-diabetic patients with non-ST-elevation myocardial infarction undergoing percutaneous coronary intervention. BMC Cardiovasc Disord. 2017;17(1):6. Marenzi G, Cosentino N, Genovese S, Campodonico J, De Metrio M, Rondinelli M, et al. Reduced Cardio-Renal Function Accounts for Most of the In-Hospital Morbidity and Mortality Risk Among Patients With Type 2 Diabetes Undergoing Primary Percutaneous Coronary Intervention for ST-Segment Elevation Myocardial Infarction. Diabetes Care. 2019;42(7):1305–11. Ertelt K, Brener SJ, Mehran R, Ben-Yehuda O, McAndrew T, Stone GW. Comparison of Outcomes and Prognosis of Patients With Versus Without Newly Diagnosed Diabetes Mellitus After Primary Percutaneous Coronary Intervention for ST-Elevation Myocardial Infarction (the HORIZONS-AMI Study). Am J Cardiol. 2017;119(12):1917–23. Oswald GA, Smith CC, Betteridge DJ, Yudkin JS. Determinants and importance of stress hyperglycaemia in non-diabetic patients with myocardial infarction. Br Med J (Clin Res Ed). 1986;293(6552):917–22. Carmen Wong KY, Wong V, Ho JT, Torpy DJ, McLean M, Cheung NW. High cortisol levels in hyperglycaemic myocardial infarct patients signify stress hyperglycaemia and predict subsequent normalization of glucose tolerance. Clin Endocrinol (Oxf). 2010;72(2):189–95. McNulty PH, Pfau S, Deckelbaum LI. Effect of plasma insulin level on myocardial blood flow and its mechanism of action. Am J Cardiol. 2000;85(2):161–5. Iliadis F, Kadoglou N, Didangelos T. Insulin and the heart. Diabetes Res Clin Pract. 2011;93(Suppl 1):S86–91. Additional Declarations No competing interests reported. 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02:11:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":437785,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between admission blood glucose in diabetic vs non-diabetic patients\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4563999/v1/93cd537e8b9af7c48cb6e1fc.png"},{"id":60198346,"identity":"7f8d65d2-7040-4982-9ec1-9dde10ec22cf","added_by":"auto","created_at":"2024-07-13 02:11:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":360894,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between mortality rate in diabetic and non-diabetic patients who have hyperglycemia on admission with acute myocardial infarction.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4563999/v1/4951d10eb7329b1ed4141a29.png"},{"id":64619294,"identity":"c5f1d819-ad87-4e78-b7af-97a82a6f1da2","added_by":"auto","created_at":"2024-09-16 16:13:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1767039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4563999/v1/6891d1c9-4297-47c2-ac34-08765397d976.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Systematic Review and Meta-analysis of the effect of Hyperglycemia on Admission for Acute Myocardial Infarction in Diabetic and non-Diabetic patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, acute coronary syndromes (ACS) constitute a major cause of mortality. The short- and long-term death rates among patients who report with acute myocardial infarction (AMI) are concerning, despite being on the decline (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). According to predictions from the American Heart Association, there will be 19\u0026nbsp;million cardiovascular deaths globally in 2020, up 18.7% from 2010. As a result, the direct burden of ACS has significantly increased in the US (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). AMI is the most severe form of ACS; nevertheless, reperfusion treatment has lately led to a drop in AMI-related mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven in the absence of preexisting diabetes, hyperglycemia can emerge during an AMI due to rising levels of catecholamines, steroids, glucagon, and falling insulin levels brought on by stress (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Twenty to fifty percent of patients with ST-segment elevation myocardial infarction (STEMI) had stress hyperglycemia at the time of admission, according to previous research (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Though the precise definition of stress hyperglycemia in relation to an AMI is unknown, it has been defined as momentarily raising plasma glucose levels in critically sick patients who have not previously been diagnosed with diabetes mellitus (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). A major factor in the instability and rupture of atherosclerotic plaques, hyperglycemia also acts as a catalyst for endothelial dysfunction and hyperinflammation (by the regulation of microRNA and apoptotic pathways, among other mechanisms) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). For example, Sardu et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) observed that stress hyperglycemia during STEMI may impact the composition of coronary thrombus, leading to enhanced inflammation as seen by raised levels of tumor necrosis factor-α in coronary thrombi taken from the hyperglycemic patients. Admission glucose, or glucose concentrations at hospital admission, has been the subject of several studies that have examined the relationship between stress hyperglycemia and both short- and long-term mortality in patients with AMI who have or do not have diabetes (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Interestingly, some of these studies have found a stronger association in patients without diabetes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Hyperglycemia during the event seems to generally increase major adverse cardiovascular events (MACE) (including re-hospitalization for heart failure, stroke, and coronary disease) in addition to mortality (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to several epidemiological research, the prevalence of hyperglycemia in patients hospitalized for ACS varies from 3\u0026ndash;71% (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Whether thrombolysis or primary percutaneous coronary intervention (pPCI) is used as a reperfusion therapy, hyperglycemia at the time of admission has been found to be a significant predictor of unfavorable outcomes in patients with AMI (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Regarding a potential relationship between diabetes and the prognostic significance of hyperglycemia in patients presenting with AMI, there is still debate. Therefore, we aimed in this study to demonstrate the effect of hyperglycemia on different outcomes in AMI patients whether they are diabetic or not.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAdhering to the Cochrane Handbook of Systematic Reviews of Interventions at each step\u0026nbsp;(19), and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement\u0026apos;s guidelines, we conducted this systematic review and meta-analysis\u0026nbsp;(20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDatabase searching\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the following search strategy: \u0026ldquo;Diabetes\u0026rdquo; or \u0026ldquo;Diabetic\u0026rdquo; AND \u0026ldquo;Acute myocardial infarction\u0026rdquo; OR \u0026ldquo;AMI\u0026rdquo; AND \u0026ldquo;hyperglycemia\u0026rdquo; OR \u0026ldquo;glucose level\u0026rdquo;, we searched PubMed, Web of Science, and Scopus for eligible articles that should undergo the screening process to determine its ability to be included in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eScreening\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter database searching, we removed the duplicates from the resulting articles using EndNote version 7\u0026nbsp;(21). software, then we uploaded the remaining articles on Rayyan software\u0026nbsp;(22)\u0026nbsp;to conduct the process of screening. First, four authors who worked independently conducted the screening by title and abstract to see the eligibility for inclusion, and then they conducted full-text screening of the included articles from the previous step. Any conflicts were referred to a senior author to resolve.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInclusion and exclusion criteria\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predetermined inclusion and exclusion criteria used for screening were any observational (cohort, cross-sectional, or case-control) and randomized controlled trials (RCT) investigating the effects of hyperglycemia in diabetic or non-diabetic AMI patients on short or long-term outcomes such as mortality and occurrence of MACE. We excluded studies that didn\u0026rsquo;t measure the effect of hyperglycemia, populations other than AMI, and case reports, case series and reviews.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality assessment\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the included observational cohort studies, we used the New Castle Ottawa scale tool provided by Cochrane for the assessment of quality. It is composed of 8 questions with a maximum of one star for each except for the comparability question that can get two stars. Therefore, the highest score is nine while the lowest score is zero. Studies scoring from 0 to 3 were considered of low quality, 4-6 were of moderate quality, and 7-9 were of high quality\u0026nbsp;(23).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData extraction\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing Microsoft Excel sheets, four independent authors conducted the process of data extraction to extract the baseline data (study design, country, sample size, groups, age, and gender) in addition to the outcomes (blood glucose on admission, odds ratio [OR], hazard ratio [HR] of mortality and MACE, mortality rate, factors affecting mortality including age, admission glucose levels, and Killip class) of the included studies. Any differences or conflicts were resolved by a senior author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing Review Manager version 5.4 software, we conducted the meta-analysis of the included studies by pooling the mean difference in continuous variables, number and total of dichotomous variables to measure the OR, and generic inverse variance of OR or HR as they were reported in the included studies. The results were considered statistically significant at p-value of less than or equal to 0.05. The used confidence intervals (CI) were 95%, and the I\u003csup\u003e2\u003c/sup\u003e was used for testing the heterogeneity with the p-value for significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSensitivity analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing OpenMetaAnalyst software, we conducted sensitivity analysis using leave-one-out method to remove the studies that caused heterogeneity in the heterogeneous outcomes.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDatabase searching and screening.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe database searching process yielded a total of 2157 articles with 761 duplicates so a total of 1396 articles entered the title and abstract screening. A total of 1369 articles were excluded and then 27 articles were screened by full-text to yield a total of 19 articles (\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) for the meta-analysis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eQuality assessment\u003c/h2\u003e \u003cp\u003eAccording to NOS, 13 studies were considered of high quality while only six studies were considered of moderate quality. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eQuality assessment of the included cohort studies using New Caste Ottawa Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepresentativeness of the exposed cohort (★)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelection of the non exposed cohort (★)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAscertainment of exposure (★)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDemonstration that outcome of interest was not present at start of study (★)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eComparability of cohorts on the basis of the design or analysis (max★★)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAssessment of outcome (★)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWas follow-up long enough for outcomes to occur? (★)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdequacy of follow up of cohorts (★)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQuality level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpur 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaolisso 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRitsinger 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDing 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchmitz 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCui 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKojima 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThoegersen 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemarchi 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRitsinger 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMamadjanov 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCui 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhou 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerreira 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJomaa 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChattopadhyay 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChattopadhyay 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYuan 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCui 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e★★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e★\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eAll the included 19 articles were cohort studies conducted in different countries including Japan, Germany, China, United Kingdom, and others. Most of the studies compared hyperglycemic AMI patients in diabetes and non-diabetes. Other studies compared either of the two mentioned groups against patients with no hyperglycemia and no diabetes. The mean age of the participants ranged from 56.3 years old to 72.3 years old. (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\u003eSummary and baseline characteristics of the included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDesign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMale, n(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpur 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e56.3 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e965 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e279 (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaolisso 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIOCA with hyperglycemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMINOCA with hyperglycemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.3 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e615 (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12 (31.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRitsinger 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo hyperglycemia or diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.7 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6927 (68.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76 (73.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDing 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo hyperglycemia or diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64.8 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.3 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e930 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e86 (76.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchmitz 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.7 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e481 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1203 (73.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCui 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.9 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.4 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1174 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e767 (76.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKojima 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.6 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.4 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e703 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e433 (72.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThoegersen 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.9 (12.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.19 (9.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e982 (75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e198 (72.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemarchi 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e65.6 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2248 (76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRitsinger 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e67 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMamadjanov 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e65\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3514 (63.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCui 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.7 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.3 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e300 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e127 (53.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhou 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.1 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.1 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e77 (81.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerreira 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.9 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.5 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e291 (68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e220 (67.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJomaa 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTunisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e60.39 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e997 (77.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChattopadhyay 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo hyperglycemia or diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e122 (70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e120 (72.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChattopadhyay 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo hyperglycemia or diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e136 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e346 (73.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYuan 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo hyperglycemia or diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.6 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.7 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e109 (74.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1466 (79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCui 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia and no diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHyperglycemia and diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61.3 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.1 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2541 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1420 (68.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis\u003c/h2\u003e \u003cp\u003eThe difference between the diabetes and non-diabetes patients regarding blood glucose level was found to be statistically significant with SMD of 1.39 (95%CI: 1.12, 1.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001) with heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;98%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHyperglycemia in diabetes patients was found to be more statistically significant associated with mortality compared to hyperglycemia in non-diabetic patients with OR of 1.47 (95%CI: 1.08, 1.99, p\u0026thinsp;=\u0026thinsp;0.01) and heterogeneity measured by I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;73%, p\u0026thinsp;=\u0026thinsp;0.005. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHyperglycemia in diabetic patients was statistically significant associated with mortality with HR of 1.92 (95%CI: 1.45, 2.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001) with heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;81%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). and OR of 1.76 (95%CI: 1.15, 2.7, p\u0026thinsp;=\u0026thinsp;0.01) with heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;78%, p\u0026thinsp;=\u0026thinsp;0.01). In non-diabetic patients admitted with AMI, hyperglycemia was statistically significant associated with mortality with HR of 1.56 (95%CI: 1.31, 1.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001), heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;72%, p\u0026thinsp;=\u0026thinsp;0.002) and OR of 2.89 (95%CI: 2.47, 3.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001) and no heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%). Moreover, hyperglycemia in diabetic patients admitted with AMI was statistically significant associated with occurrence of MACE with HR of 1.9 (95%CI: 1.19, 3.03, p\u0026thinsp;=\u0026thinsp;0.007) and heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;83%, p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eIn addition, hyperglycemia in non-diabetic AMI patients was statistically significant associated with occurrence of MACE with HR of 1.6 (95%CI: 1.15, 2.23, p\u0026thinsp;=\u0026thinsp;0.006) and heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;89%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001). Age was among the factors that predicted mortality after hyperglycemia in diabetic and non-diabetic AMI patients with HR of 1.05 (1.04, 1.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001), and no heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%) in diabetic patients and HR of 1.07 (95%CI: 1.02, 1.12, p\u0026thinsp;=\u0026thinsp;0.01) and heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;84%, p\u0026thinsp;=\u0026thinsp;0.01) in non-diabetic patients. It was observed that increased glucose levels on admission are statistically significant predictors of mortality in diabetic and non-diabetic patients with OR of 4.7 (95%CI: 1.48, 14.91, p\u0026thinsp;=\u0026thinsp;0.009) and no heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%) and OR of 1.88 (95%CI: 1.52, 2.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001) and no heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%), respectively.\u003c/p\u003e \u003cp\u003eKillip class\u0026thinsp;\u0026ge;\u0026thinsp;2 was statistically significant associated with mortality in non-diabetic AMI patients admitted with hyperglycemia with HR of 1.9 (95%CI: 1.4, 2.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and non-significant heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;42%, p\u0026thinsp;=\u0026thinsp;0.18), while no significant association was observed between Killip class and mortality in diabetic patients with HR of 0.94 (95%CI: 0.62, 1.42, p\u0026thinsp;=\u0026thinsp;0.76).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eAfter conducting leave-one-out analysis for blood glucose levels comparison among the diabetic and non-diabetic patients, it was found that Cui et al. (2022) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), and Kojima et al. (2019) (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) were the main sources of heterogeneity. For the comparison between the diabetic and non-diabetic patients regarding mortality, it was observed that Schmitz et al. (2022) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) was the main source of heterogeneity.\u003c/p\u003e \u003cp\u003eFor the mortality outcome in diabetic patients using HR, Cui et al. (2021) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), and Kojima et al. (2019) (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) were considered the main reasons for heterogeneity. While using OR, Cui et al. (2023) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) was the main source of heterogeneity. For the mortality outcome in non-diabetic patients using HR, Cui et al. (2021) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) was considered the main source for heterogeneity. Regarding the occurrence of MACE in diabetic patients, Cui et al. (2022) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) caused the heterogeneity in the outcome, while Ristinger et al. (2021) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) caused the heterogeneity in the MACE outcome of non-diabetics. Yuan et al. (2022) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) was observed to be the main cause of heterogeneity in the association of age with mortality using OR.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study showed that compared hyperglycemia was more evident in diabetic patients on their admission with AMI compared to non-diabetic patients and this is due to their already present hyperglycemia due to diabetes which increases during AMI. We demonstrated that hyperglycemia in AMI was significantly associated with increased mortality whatever the diabetes status was. However, diabetic patients were more likely to have increased mortality rates compared to non-diabetic patients. Moreover, some factors were associated with increased mortality in AMI patients admitted with hyperglycemia such as older age, admission glucose, and Killip class. It is clear from the present results that by increasing glucose levels, the mortality rates and MACE will increase whether the patient is diabetic or not.\u003c/p\u003e \u003cp\u003eBy upregulating gluconeogenesis and glycogenolysis, the rising levels of glucagon, cortisol, and cytokines in the context of AMI facilitate the synthesis of glucose (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Stress hyperglycemia is a common condition because decreased insulin production by pancreatic β-cells was unable to counteract the hyperglycemic effects of these counter-regulatory hormones and cytokines (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Even worse, by releasing free fatty acids from adipose tissue and inducing serine/threonine kinases that disrupt insulin signaling, the sympathetic nervous system's activation causes insulin resistance (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Stress hyperglycemia generally causes prothrombosis, oxidative stress, inflammation, endothelial dysfunction, reduction in coronary flow, enlargement of infarct size, and compromised heart function. A study involving 460 patients diagnosed with STEMI, for instance, revealed that individuals with hyperglycemia had a lower incidence of Thrombolysis In Myocardial Infarction (TIMI) flow grade 3 prior to initial PCI (12% vs. 28%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImproving glucose levels enhances hospitalized patients' clinical results. The ideal course of treatment for hyperglycemia is debatable, though. The American College of Endocrinology suggested in 2009 that blood glucose levels in critically ill hospitalized patients be kept at 110 mg/d, citing research by Leuven as support (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). The American Diabetes Association (ADA) advised in 2020 that critically sick patients with blood glucose levels higher than 180 mg/dL be given insulin therapy, with a target blood glucose range of 140\u0026ndash;180 mg/dL (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). An upper glucose limit of 180 mg/dL is recommended by a prior study for critically sick individuals who do not have diabetes (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Furthermore, when determining the target glucose range, comorbidities need to be taken into account.\u003c/p\u003e \u003cp\u003eThere is currently no set cutoff value that can be used effectively, despite the fact that mounting data indicates that hyperglycemia has a poor prognosis in AMI patients regardless of whether they have diabetes. A blood glucose level more than 7.78 mmol/L was considered acute hyperglycemia in the study by Khalfallah et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), however patients in the study by Li et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) were categorized based on a threshold of 11.11 mmol/L. The term \"admission hyperglycemia\" was not defined consistently in these studies, and it should not be confused with diabetes mellitus, impaired fasting blood glucose (FBG), or aberrant glucose tolerance. More importantly, since patients with and without diabetes have distinct basal blood glucose concentrations and varying degrees of acute rise during stress, there is no justification for utilizing the same cut-off value for both groups. Suleiman et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) discovered that fasting and immediate blood glucose levels were independent predictors of the prognosis for AMI; however, fasting hyperglycemia had a stronger predictive value than immediate hyperglycemia. In order to prevent diabetes from being confused with undiagnosed diabetes, Cui et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) used the blood glucose level as the FBG at admission and the diagnosis of diabetes at the time of discharge. As a result, our approach of predicting blood glucose levels to predict the clinical prognosis of AMI patients was more logical and scientific.\u003c/p\u003e \u003cp\u003eThe prognostic significance of admission hyperglycemia in individuals with and without diabetes has been the main focus of previous research (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). According to certain research, entry blood glucose was not a more reliable or strong independent predictor of risk than persistent hyperglycemia. Hyperglycemia, defined as blood glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;8.9 mmol/L, is linked to pre-discharge left ventricular dysfunction and reduced myocardial perfusion even in cases where the infarct-related artery is still open 24 hours after the onset of symptoms (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). According to a different study, high glucose at admission is not as strongly correlated with 30-day MACE as persistent hyperglycemia following myocardial infarction (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). In a prior study, FBG outperformed admission glucose in terms of 30-day mortality (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). The advantage of FBG over random glucose levels in predicting the outcome is likely due to various factors, including variations in caloric intake and duration since the last meal. FBG was an independent risk factor for the Gensini score in AMI patients, and random blood glucose and FBG were positively linked with the Gensini score in patients with AMI in a prior study (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). These results show that rather than an underlying \"diabetic state,\" rapid and persistent rise of blood glucose may cause microvascular dysfunction and worsen outcomes. Greater entry glucose concentrations were associated with higher mortality in 1219 non-diabetic individuals following AMI, but larger glucose decreases during a 24-hour period were associated with decreased 30-day mortality. Both the baseline glucose and the 24-hour glucose change were still highly significant indicators of death after 180 days (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a result, it was thought that FBG levels at admission might be a better indicator of the persistence of blood glucose rise and a stronger prospective predictor of clinical outcomes in patients with AMI. There is no precise blood glucose cutoff value for AMI patients to anticipate adverse events, nor is there a precise definition for admission hyperglycemia in AMI patients. Hemoglobin A1c (HbA1c), the glucose-HbA1c ratio (GHR), and the stress hyperglycemia ratio (SHR) have all been linked to favorable clinical outcomes in AMI and other disease processes. However, because thresholds have been chosen at random, their ideal values remain unclear (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCui et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) demonstrated that admission glucose had a poor predictive power for long-term mortality in patients with and without diabetes, with area under the ROC curve values of just 0.634 and 0.660, respectively. This finding may have been impacted by the small sample size. On the other hand, the study's research methodology and research questions may offer useful information for next investigations. According to a recent study by Hao et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e), entry hyperglycemia may be a reliable indicator of an AMI patient's outcome. The difference in predictive value between the patient groups with and without diabetes was not examined in the study, though. In patients with AMI, admission hyperglycemia had a predictive value for hospital mortality, according to another study9, although the predicted value varied depending on whether the patient had diabetes or not. Cui et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) conducted a long-term follow-up and made a distinction between patients with and without diabetes, in contrast to the aforementioned analysis. The findings demonstrated that admission hyperglycemia did, in fact, have a different effect on patients with and without diabetes, since it was significantly linked to a worse long-term prognosis in the former group while having no effect on the latter. One rationale is that the presence of AMI in patients with diabetes may disguise the effects of admission hyperglycemia, which in turn may lead to a poor long-term prognosis (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Admission hyperglycemia is a sign of serious myocardial infarction and cardiac dysfunction in AMI patients without diabetes (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). individuals without diabetes may have higher blood glucose due to greater ischemia or hemodynamic damage than individuals with diabetes, whose blood glucose levels are generally elevated (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Another explanation is that because the hyperglycemia group of AMI patients without diabetes tends to have greater levels of brain natriuretic polypeptide and much higher C-reactive protein, they suffered a more widespread myocardial infarction. The third theory is that insulin treatment for diabetes patients was more common. Appropriate insulin therapy can improve cardiac perfusion, according to studies. It has been demonstrated that insulin increases the synthesis of nitric oxide by activating endothelial nitric oxide synthase (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Furthermore, insulin may reduce cardiac cell apoptosis in ischemia patients, among other positive effects (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has some limitations including that all of the included studies are of observational design which has higher risk of bias than RCTs. There is no common definition for hyperglycemia and no common cut off value that are relied one. Therefore, further RCTs are required in addition to test accuracy of the cut off value that should be used for prognosis whether in diabetic or non-diabetic patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHyperglycemia in AMI patients is a predictor of worse outcomes including MACE, and mortality whether these patients are diabetic or not. Some factors act as predictors for mortality in these patients including older age, higher glucose levels on admission, and high Killip class. However, further studies are required to put a definite value for hyperglycemia and cut off for prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of Data:\u003c/strong\u003e This study involved the collection and analysis of data from previously published studies. No new human or animal subjects were involved directly in this research. All included studies were required to have obtained appropriate ethical approval and informed consent from participants as indicated in their respective publications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate and Publish:\u003c/strong\u003e Since this research is a secondary analysis of existing data, direct consent to participate and publish was not required from individual subjects. However, the original studies included in this review and meta-analysis documented their consent procedures, ensuring compliance with ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Integrity and Confidentiality:\u003c/strong\u003e Efforts were made to ensure the integrity and confidentiality of data by including only publicly available and anonymized data from published studies. Any sensitive information from the original studies was handled in accordance with ethical guidelines to protect the privacy of individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuidelines Followed\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eGuidelines Followed:\u003c/strong\u003e PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor any further information or clarifications, please contact Alawaji, Reem ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. All expenses related to the study were covered by the authors themselves.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors have contributed significantly to the study and manuscript preparation, and all authors are in agreement with the content of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSteg PG, James SK, Atar D, Badano LP, Bl\u0026ouml;mstrom-Lundqvist C, Borger MA, et al. ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J. 2012;33(20):2569\u0026ndash;619.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuntaine AJ, Shah B, Lorin JD, Sedlis SP. Revascularization Strategies in Patients with Diabetes Mellitus and Acute Coronary Syndrome. 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Diab Vasc Dis Res. 2019;16(6):582\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemarchi A, Cornara S, Somaschini A, Fortuni F, Mandurino-Mirizzi A, Crimi G, et al. Has hyperglycemia a different prognostic role in STEMI patients with or without diabetes? Nutr Metab Cardiovasc Dis. 2021;31(2):528\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThoegersen M, Josiassen J, Helgestad OK, Berg Ravn H, Schmidt H, Holmvang L, et al. The association of diabetes and admission blood glucose with 30-day mortality in patients with acute myocardial infarction complicated by cardiogenic shock. Eur Heart J Acute Cardiovasc Care. 2020;9(6):626\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKojima T, Hikoso S, Nakatani D, Suna S, Dohi T, Mizuno H, et al. Impact of Hyperglycemia on Long-Term Outcome in Patients With ST-Segment Elevation Myocardial Infarction. Am J Cardiol. 2020;125(6):851\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui K, Fu R, Yang J, Xu H, Yin D, Song W, et al. Admission Blood Glucose and 2-Year Mortality After Acute Myocardial Infarction in Patients With Different Glucose Metabolism Status: A Prospective, Nationwide, and Multicenter Registry. Front Endocrinol (Lausanne). 2022;13:898384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing XS, Wu SS, Chen H, Zhao XQ, Li HW. High admission glucose levels predict worse short-term clinical outcome in non-diabetic patients with acute myocardial infraction: a retrospective observational study. BMC Cardiovasc Disord. 2019;19(1):163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitsinger V, Hagstr\u0026ouml;m E, Lagerqvist B, Norhammar A. Admission Glucose Levels and Associated Risk for Heart Failure After Myocardial Infarction in Patients Without Diabetes. J Am Heart Assoc. 2021;10(22):e022667.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaolisso P, Fo\u0026agrave; A, Bergamaschi L, Angeli F, Fabrizio M, Donati F, et al. Impact of admission hyperglycemia on short and long-term prognosis in acute myocardial infarction: MINOCA versus MIOCA. Cardiovasc Diabetol. 2021;20(1):192.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShamoon H, Hendler R, Sherwin RS. Synergistic interactions among antiinsulin hormones in the pathogenesis of stress hyperglycemia in humans. J Clin Endocrinol Metab. 1981;52(6):1235\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshizuka K, Usui I, Kanatani Y, Bukhari A, He J, Fujisaka S, et al. Chronic tumor necrosis factor-alpha treatment causes insulin resistance via insulin receptor substrate-1 serine phosphorylation and suppressor of cytokine signaling-3 induction in 3T3-L1 adipocytes. Endocrinology. 2007;148(6):2994\u0026ndash;3003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026uuml;nser MW, Hasibeder WR. Sympathetic overstimulation during critical illness: adverse effects of adrenergic stress. J Intensive Care Med. 2009;24(5):293\u0026ndash;316.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartnik M, Malmberg K, Hamsten A, Efendic S, Norhammar A, Silveira A, et al. Abnormal glucose tolerance\u0026ndash;a common risk factor in patients with acute myocardial infarction in comparison with population-based controls. J Intern Med. 2004;256(4):288\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallander M, Bartnik M, Efendic S, Hamsten A, Malmberg K, Ohrvik J, et al. Beta cell dysfunction in patients with acute myocardial infarction but without previously known type 2 diabetes: a report from the GAMI study. Diabetologia. 2005;48(11):2229\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein J, Fasshauer M, Ito M, Lowell BB, Benito M, Kahn CR. beta(3)-adrenergic stimulation differentially inhibits insulin signaling and decreases insulin-induced glucose uptake in brown adipocytes. J Biol Chem. 1999;274(49):34795\u0026ndash;802.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelley DE, Mokan M, Simoneau JA, Mandarino LJ. Interaction between glucose and free fatty acid metabolism in human skeletal muscle. J Clin Invest. 1993;92(1):91\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimmer JR, Ottervanger JP, de Boer MJ, Dambrink JH, Hoorntje JC, Gosselink AT, et al. Hyperglycemia is an important predictor of impaired coronary flow before reperfusion therapy in ST-segment elevation myocardial infarction. J Am Coll Cardiol. 2005;45(7):999\u0026ndash;1002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoghissi ES, Korytkowski MT, DiNardo M, Einhorn D, Hellman R, Hirsch IB, et al. American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Endocr Pract. 2009;15(4):353\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e15. Diabetes Care in the Hospital: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S193\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIchai C, Preiser JC. International recommendations for glucose control in adult non diabetic critically ill patients. Crit Care. 2010;14(5):R166.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalfallah M, Abdelmageed R, Elgendy E, Hafez YM. Incidence, predictors and outcomes of stress hyperglycemia in patients with ST elevation myocardial infarction undergoing primary percutaneous coronary intervention. Diab Vasc Dis Res. 2020;17(1):1479164119883983.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Li X, Zhang Y, Zhang L, Wu Q, Bai Z, et al. Impact of glycemic control status on patients with ST-segment elevation myocardial infarction undergoing percutaneous coronary intervention. BMC Cardiovasc Disord. 2020;20(1):36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuleiman M, Hammerman H, Boulos M, Kapeliovich MR, Suleiman A, Agmon Y, et al. Fasting glucose is an important independent risk factor for 30-day mortality in patients with acute myocardial infarction: a prospective study. Circulation. 2005;111(6):754\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim EJ, Jeong MH, Kim JH, Ahn TH, Seung KB, Oh DJ, et al. Clinical impact of admission hyperglycemia on in-hospital mortality in acute myocardial infarction patients. Int J Cardiol. 2017;236:9\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSia CH, Chan MH, Zheng H, Ko J, Ho AF, Chong J, et al. Optimal glucose, HbA1c, glucose-HbA1c ratio and stress-hyperglycaemia ratio cut-off values for predicting 1-year mortality in diabetic and non-diabetic acute myocardial infarction patients. Cardiovasc Diabetol. 2021;20(1):211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosuge M, Kimura K, Ishikawa T, Shimizi T, Hibi K, Toda N, et al. Persistent hyperglycemia is associated with left ventricular dysfunction in patients with acute myocardial infarction. Circ J. 2005;69(1):23\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Horst IC, Nijsten MW, Vogelzang M, Zijlstra F. Persistent hyperglycemia is an independent predictor of outcome in acute myocardial infarction. Cardiovasc Diabetol. 2007;6:2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin X, Shao L, Zhang L, Ma L, Xiong S. Investigation of Interaction between Vitamin D Receptor Gene Polymorphisms and Environmental Factors in Early Childhood Caries in Chinese Children. Biomed Res Int. 2019;2019:4315839.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoyal A, Mahaffey KW, Garg J, Nicolau JC, Hochman JS, Weaver WD, et al. Prognostic significance of the change in glucose level in the first 24 h after acute myocardial infarction: results from the CARDINAL study. Eur Heart J. 2006;27(11):1289\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeedwania P, Kosiborod M, Barrett E, Ceriello A, Isley W, Mazzone T, et al. Hyperglycemia and acute coronary syndrome: a scientific statement from the American Heart Association Diabetes Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation. 2008;117(12):1610\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts GW, Quinn SJ, Valentine N, Alhawassi T, O'Dea H, Stranks SN, et al. Relative Hyperglycemia, a Marker of Critical Illness: Introducing the Stress Hyperglycemia Ratio. J Clin Endocrinol Metab. 2015;100(12):4490\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao Y, Lu Q, Li T, Yang G, Hu P, Ma A. Admission hyperglycemia and adverse outcomes in diabetic and non-diabetic patients with non-ST-elevation myocardial infarction undergoing percutaneous coronary intervention. BMC Cardiovasc Disord. 2017;17(1):6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarenzi G, Cosentino N, Genovese S, Campodonico J, De Metrio M, Rondinelli M, et al. Reduced Cardio-Renal Function Accounts for Most of the In-Hospital Morbidity and Mortality Risk Among Patients With Type 2 Diabetes Undergoing Primary Percutaneous Coronary Intervention for ST-Segment Elevation Myocardial Infarction. Diabetes Care. 2019;42(7):1305\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErtelt K, Brener SJ, Mehran R, Ben-Yehuda O, McAndrew T, Stone GW. Comparison of Outcomes and Prognosis of Patients With Versus Without Newly Diagnosed Diabetes Mellitus After Primary Percutaneous Coronary Intervention for ST-Elevation Myocardial Infarction (the HORIZONS-AMI Study). Am J Cardiol. 2017;119(12):1917\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOswald GA, Smith CC, Betteridge DJ, Yudkin JS. Determinants and importance of stress hyperglycaemia in non-diabetic patients with myocardial infarction. Br Med J (Clin Res Ed). 1986;293(6552):917\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmen Wong KY, Wong V, Ho JT, Torpy DJ, McLean M, Cheung NW. High cortisol levels in hyperglycaemic myocardial infarct patients signify stress hyperglycaemia and predict subsequent normalization of glucose tolerance. Clin Endocrinol (Oxf). 2010;72(2):189\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNulty PH, Pfau S, Deckelbaum LI. Effect of plasma insulin level on myocardial blood flow and its mechanism of action. Am J Cardiol. 2000;85(2):161\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIliadis F, Kadoglou N, Didangelos T. Insulin and the heart. Diabetes Res Clin Pract. 2011;93(Suppl 1):S86\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, acute myocardial infarction, glucose, hyperglycemia","lastPublishedDoi":"10.21203/rs.3.rs-4563999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4563999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eRegarding a potential relationship between diabetes and the prognostic significance of hyperglycemia in patients presenting with acute myocardial infarction (AMI), there is still debate. Therefore, we aimed in this study to demonstrate the effect of hyperglycemia on different outcomes in AMI patients whether they are diabetic or not.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing the following search strategy: \u0026ldquo;Diabetes\u0026rdquo; or \u0026ldquo;Diabetic\u0026rdquo; AND \u0026ldquo;Acute myocardial infarction\u0026rdquo; OR \u0026ldquo;AMI\u0026rdquo; AND \u0026ldquo;hyperglycemia\u0026rdquo; OR \u0026ldquo;glucose level\u0026rdquo;, we searched PubMed, Web of Science, and Scopus for eligible articles that should undergo the screening process to determine its ability to be included in our study. Using Review Manager version 5.4 software, we conducted the meta-analysis of the included studies by pooling the mean difference in continuous variables, number and total of dichotomous variables to measure the odds ratio (OR), and generic inverse variance of OR or hazard ratio (HR) as they were reported in the included studies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe difference between the diabetes and non-diabetes patients regarding blood glucose level was found to be statistically significant with SMD of 1.39 (95%CI: 1.12, 1.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001). Hyperglycemia in diabetic patients was statistically significant associated with mortality with HR of 1.92 (95%CI: 1.45, 2.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001) and OR of 1.76 (95%CI: 1.15, 2.7, p\u0026thinsp;=\u0026thinsp;0.01). In non-diabetic patients admitted with AMI, hyperglycemia was statistically significant associated with mortality with HR of 1.56 (95%CI: 1.31, 1.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001), and OR of 2.89 (95%CI: 2.47, 3.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001). Moreover, hyperglycemia in diabetic patients admitted with AMI was statistically significant associated with occurrence of MACE with HR of 1.9 (95%CI: 1.19, 3.03, p\u0026thinsp;=\u0026thinsp;0.007) and hyperglycemia in non-diabetic AMI patients was statistically significant associated with occurrence of MACE with HR of 1.6 (95%CI: 1.15, 2.23, p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHyperglycemia in AMI patients is a predictor of worse outcomes including MACE, and mortality whether these patients are diabetic or not. Some factors act as predictors for mortality in these patients including older age, higher glucose levels on admission, and high Killip class.\u003c/p\u003e","manuscriptTitle":"A Systematic Review and Meta-analysis of the effect of Hyperglycemia on Admission for Acute Myocardial Infarction in Diabetic and non-Diabetic patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-13 02:11:30","doi":"10.21203/rs.3.rs-4563999/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-10T14:56:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-09T16:03:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-05T13:35:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-05T09:45:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-05T06:47:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79030916328347932673792431910475875075","date":"2024-07-02T16:22:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5791023393414361621175186168652945534","date":"2024-07-02T16:01:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143920515676786590832996920732013816805","date":"2024-07-02T14:01:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72032714555831363739956112172883891227","date":"2024-07-02T09:05:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303871313186293960325423080246192032769","date":"2024-07-02T06:39:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94144229645853778725116292123448762506","date":"2024-07-02T04:00:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45173913737954672909965850175483388039","date":"2024-07-02T03:03:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33530516222641934055209748430034014990","date":"2024-07-02T01:39:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298241211385315658288306026312141746925","date":"2024-07-01T23:23:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19056132008536109220955882219380280420","date":"2024-07-01T20:32:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122299387509386674857909195710873921219","date":"2024-07-01T20:25:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-01T20:23:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-15T05:11:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-15T05:09:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2024-06-11T12:12:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3788c7f-b9ea-4425-90bf-82a32f3e3523","owner":[],"postedDate":"July 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:05:45+00:00","versionOfRecord":{"articleIdentity":"rs-4563999","link":"https://doi.org/10.1186/s13098-024-01459-w","journal":{"identity":"diabetology-and-metabolic-syndrome","isVorOnly":false,"title":"Diabetology \u0026 Metabolic Syndrome"},"publishedOn":"2024-09-12 15:58:18","publishedOnDateReadable":"September 12th, 2024"},"versionCreatedAt":"2024-07-13 02:11:30","video":"","vorDoi":"10.1186/s13098-024-01459-w","vorDoiUrl":"https://doi.org/10.1186/s13098-024-01459-w","workflowStages":[]},"version":"v1","identity":"rs-4563999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4563999","identity":"rs-4563999","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}