Association between glycemia and multi-vessel lesion in participants undergoing percutaneous coronary intervention: A cross-sectional study

Association between glycemia and multi-vessel lesion in participants undergoing percutaneous coronary intervention: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between glycemia and multi-vessel lesion in participants undergoing percutaneous coronary intervention: A cross-sectional study Hezeng Dong, Zhaozheng Liu, Hao Chen, Rui Shi, Qu Jin, Xiao Shao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3893811/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aims to elucidate the association between glycemia and the occurrence of multi-vessel lesions in participants undergoing Percutaneous Coronary Intervention (PCI). Methods A cohort of 2,533 patients with coronary heart disease, treated with drug-eluting stents, was analysed. Of these, 1,973 patients, identified by the endpoint of multi-vessel lesions, were examined using univariate and multivariate logistic regression analyses to determine the relationship between glycemia levels and multi-vessel lesion occurrence. Results The analysis included 1,973 participants, among whom 474 patients were identified with coronary multi-vessel lesions. Univariate logistic regression analysis demonstrated a positive correlation between glycemia and the occurrence of coronary multi-vessel lesions (OR 1.04; 95% CI 1.01–1.08; p = 0.02)..The adjusted model indicated that for each unit increase in glycemia, the risk of developing coronary multi-vessel lesions increased by 4%, showing a significant correlation (p < 0.05). Subgroup analyses revealed that the impact of glycemia on multi-vessel lesions in patients with PCI varied according to gender, age, and smoking status, with the effect being more pronounced in men, older patients, and smokers。 Conclusion Our findings establish a significant association between glycemia and the incidence of multi-vessel lesions, particularly pronounced in male patients, individuals over 45, and smokers. Health sciences/Cardiology Health sciences/Endocrinology Health sciences/Medical research Glycemia Multi-Vessel Lesion Percutaneous Coronary Intervention Figures Figure 1 Figure 2 Figure 3 Background As advancements in coronary interventions continue [ 1 ] , the evolution of intravascular imaging and functional techniques is notable [ 2 ] , leading to more frequent diagnosis of multi-vessel lesions. The European Society of Cardiology (ESC) reports that over 50% of patients with ST-Elevation Myocardial Infarction (STEMI) present with concomitant multi-vessel lesions [ 3 ] . These lesions significantly predict Major Adverse Cardiovascular Events (MACCE), and patients with STEMI and multi-vessel lesions are at increased risk of recurrent cardiovascular events [ 4 ] . Preventing multi-vessel lesions in clinical practice, reducing acute coronary syndromes, mitigating cardiovascular mortality risk, and developing individualised preventative protocols are crucial research areas. Diabetes mellitus and its complications are widely recognized as significant risk factors for coronary artery disease [ 5 ] . The relationship between glycemia and multi-vessel lesions warrants further investigation. Recent research has shifted focus to glycemic control in diabetic patients undergoing Percutaneous Coronary Intervention (PCI). For example, Joseph B Muhlestein, Jeffrey L Anderson et al. found a marked increase in mortality risk linked to slight rises in fasting glycemia in patients with Coronary Artery Disease (CAD) undergoing PCI, even with hemodialysis. This finding highlights the critical importance of early detection and management of glycemia-related risks [ 6 ] . Additionally, Djupsjö, Kuhl et al. reported nearly double the long-term cardiovascular mortality and more than twice the incidence of cardiovascular events in patients with hyperglycemia [ 7 ] . Research by A F Zand Parsa et al., involving 125 patients undergoing coronary angiography (group 1), compared to a control group (group 2) matched for age and gender but without proximal lesions, found that proximal and multivessel coronary artery involvement is associated with a history of diabetes, but not with high cholesterol, hypertension, smoking, or hypertriglyceridemia [ 8 ] .While these studies provide valuable insights, direct evidence correlating glycemia with multi-vessel lesions, particularly in Asian populations, is limited. This gap is especially relevant considering the unique lifestyle and genetic characteristics of Asian populations. Our study aims to address this deficiency by exploring the glycemia-multi-vessel lesion connection in a cross-sectional cohort of patients undergoing PCI. The objective is to provide clinicians with more precise treatment protocols and to enhance the scientific basis for cardiovascular risk management in diabetic patients. Method Our study examined a cohort of patients who underwent Percutaneous Coronary Intervention (PCI) at our institution from July 2009 to August 2011, with a follow-up period tracking long-term outcomes (total n = 2533). Selection was based on stringent inclusion criteria. The study encompassed a comprehensive review of coronary angiography records, conforming to the highest clinical standards. After excluding incomplete or ambiguous data, the analysis incorporated 1973 patients' records (Fig. 1). The procedural approaches during PCI, including stent selection and use of intravascular ultrasound, were at the discretion of the attending clinicians [ 9 ] . These choices strictly adhered to current clinical guidelines and best practices, and with the consent of all participants. The primary endpoint was the presence of ≥ 50% stenosis in at least two of the three major epicardial vessels [ 10 ] , ascertained through detailed coronary angiographic assessment using advanced imaging techniques for a precise evaluation of the coronary vasculature. Moreover, the comprehensive collection of demographic and clinical data was pivotal to our analysis. We meticulously recorded patient demographics, medical history, smoking status, and other pertinent clinical parameters at admission. These included detailed assessments of smoking history, diabetes mellitus, and hypertension, all defined according to established clinical criteria. Diabetes and hypertension were diagnosed based on standardized clinical and laboratory criteria. Patients were classified as diabetic if their fasting glycemia concentration exceeded 6.1 mmol/L, their glycosylated hemoglobin level surpassed 6.5%, or they were receiving insulin or oral hypoglycemic medications. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medication. A smoking history was considered as tobacco use within the preceding ten years. Glycemia values were obtained from fasting blood samples at the time of admission and all other laboratory tests including (Cr,UA,BIL, TC,TG,HDL-C,LDL-C) were also obtained from fasting blood samples at the time of admission. Lesion characteristics (multi-vessel lesions) were determined by performing coronary angiography. Ethics approval and consent to participate All data were collected retrospectively using a standardised data collection form. Follow-up information was collected through outpatient, readmission, or telephone contacts and all methods were performed in accordance with the relevant guidelines and regulations [11] Study data were sourced from the dryad database of the First Affiliated Hospital of Zhengzhou University,courtesy of Yao,Hai Mu et al.and are aeeessible via http://doi.org/10.5061/drayd.13d31.The Ethics Committee of the First Affiliated Hospital of Zhengzhou University has endorsed the public policy statement associated with the dataset, eliminating the need for an ethics statement in this study. Statistical analysis In our study, glycemia levels of participants were categorized into four quartiles: Quartile 1 (n = 482), Quartile 2 (n = 500), Quartile 3 (n = 496), and Quartile 4 (n = 495). Participant characteristics were summarized. Categorical variables were presented as numbers (n) and percentages (%), evaluated using chi-square tests. Continuous variables were expressed as mean ± standard deviation or median (interquartile range) for normally distributed data. We conducted one-way and multifactorial linear regression analyses to examine the relationship between glycemia and multi-vessel lesion. Variables selected based on a p-value <0.05 in univariate analysis, previous literature, or clinical relevance, including age, sex, smoking, hypertension, DBP, HR, UA, and TG, were adjusted for in the multifactorial logistic analysis for the overall population. Subgroup analyses utilized logistic models to ascertain the stability of the glycemia-multi-vessel lesion association across subgroups, including gender, age, and smoking status. All analyses were conducted using Free Statis Approximatics software version 1.9. A two-sided P value <0.05 was deemed statistically significant. Results Study Population and Baseline Characteristics Our comprehensive study involved 2,533 patients diagnosed with coronary artery disease undergoing Percutaneous Coronary Intervention (PCI). After rigorous data screening, 1,973 participants were included in the final analysis. The cohort comprised 1,341 men and 632 women, encompassing a wide demographic range. Detailed examination of baseline characteristics identified notable associations between glycemia and several key factors, such as gender, age, hypertension, diabetes mellitus, and the prevalence of multi-vessel lesions (Table1). These insights highlight the complex relationship between metabolic parameters and cardiovascular disease, emphasizing the need for comprehensive patient evaluation and management in clinical practice. Table1 Baseline characteristics of the study participants. Variables All participants (n = 1973) Quartile Glycemia1 (n = 482) Quartile Glycemia2 (n = 500) Quartile Glycemia3 (n = 496) Quartile Glycemia4 (n = 495) P statistic sex, n (%) 0.004 13.241 Female 632 (32.0) 135 (28) 144 (28.8) 168 (33.9) 185 (37.4) Male 1341 (68.0) 347 (72) 356 (71.2) 328 (66.1) 310 (62.6) Age(years) Mean ± SD 59.9 ± 11.1 58.7 ± 12.1 59.7 ± 11.0 60.6 ± 10.9 60.8 ± 10.2 0.013 3.592 Hypertension n (%) 0.001 16.082 No 975 (49.4) 264 (54.8) 261 (52.2) 238 (48) 212 (42.8) Yes 998 (50.6) 218 (45.2) 239 (47.8) 258 (52) 283 (57.2) DM, n (%) < 0.001 492.204 No 1553 (78.7) 448 (92.9) 466 (93.2) 422 (85.1) 217 (43.8) Yes 420 (21.3) 34 (7.1) 34 (6.8) 74 (14.9) 278 (56.2) smoking, n (%) 0.06 7.398 No 1322 (67.0) 312 (64.7) 319 (63.8) 340 (68.5) 351 (70.9) Yes 651 (33.0) 170 (35.3) 181 (36.2) 156 (31.5) 144 (29.1) SBP(mmHg) Mean ± SD 104.5 ± 28.5 108.8 ± 28.1 102.5 ± 28.2 106.6 ± 29.0 100.1 ± 28.0 < 0.001 9.372 DBP(mmHg) Mean ± SD 77.3 ± 11.9 78.0 ± 11.6 76.0 ± 11.6 77.4 ± 12.1 78.0 ± 12.2 0.031 2.964 Heart.rate Mean ± SD 72.1 ± 11.5 69.8 ± 10.8 71.1 ± 10.1 73.0 ± 11.6 74.4 ± 12.9 < 0.001 15.778 Cr(μmol/L) Mean ± SD 72.0 ± 30.2 73.3 ± 25.5 72.3 ± 20.5 73.0 ± 40.1 69.2 ± 31.1 0.133 1.867 UA(μmol/L) Mean ± SD 304.2 ± 92.5 306.4 ± 87.0 308.0 ± 84.3 310.4 ± 100.5 291.9 ± 96.3 0.007 4.034 BIL(mg/dL) Mean ± SD 9.8 ± 7.6 9.4 ± 4.6 9.5 ± 5.2 10.4 ± 12.3 10.0 ± 5.7 0.16 1.723 TC(Mmol/L) Mean ± SD 4.3 ± 1.1 4.1 ± 1.0 4.2 ± 1.0 4.3 ± 1.1 4.4 ± 1.1 < 0.001 9.736 TG(Mmol/L) Mean ± SD 1.9 ± 1.4 1.6 ± 0.8 1.8 ± 1.2 2.1 ± 1.9 2.2 ± 1.4 < 0.001 14.313 HDL.C(Mmol/L)Mean ± SD 1.1 ± 0.3 1.1 ± 0.3 1.1 ± 0.3 1.1 ± 0.3 1.0 ± 0.3 0.215 1.493 LDL.C(Mmol/L) Mean ± SD 2.7 ± 0.9 2.5 ± 0.9 2.7 ± 0.9 2.7 ± 0.9 2.8 ± 1.0 < 0.001 6.921 multi.vessel n (%) 0.003 13.998 No 1499 (76.0) 392 (81.3) 379 (75.8) 376 (75.8) 352 (71.1) Yes 474 (24.0) 90 (18.7) 121 (24.2) 120 (24.2) 143 (28.9) Data are shown as mean ± standard deviation (SD) or median (IQR) for continuous variables and proportions (%) for categorical variables Sex,age,hypertension, DM,smoking,SBP, DBP,heart.rate,Cr, UA, BIL,TC, HDL,C, LDL.C,multi-vessel lesion P values in bold are < 0.05 Univariate and Multivariate Analysis In univariate analysis, factors such as age, hypertension, diabetes, blood glucose levels, uric acid levels, and triglycerides significantly correlated with coronary artery multi-vessel lesions. (Table 2). Table2：Univariate analysis for overall population Variable OR_95CI P_value Sex=female,n (%) 0.94 (0.75~ 1.17) 0.56 Age(years) 1.03 (1.02~ 1.04) <0.001 Hypertension, n (%) 1.31 (1.06~ 1.61) 0.011 DM,n (%) 1.85 (1.46~2.34) <0.001 Smoking,n (%) 0.97 (0.78~ 1.21) 0.788 SBP(mmHg) 1 (1~ 1.01) 0.317 DBP(mmHg) 1.01 (1~ 1.02) 0.012 Heart.rate(Bpm) 1.01 (1~ 1.02) 0.13 Glycemia(Mmol/L) 1.04 (1.01~ 1.08) 0.02 Cr(μmol/L) 1 (1~ 1) 0.358 UA(μmol/L) 1 (1~ 1) 0.023 BIL(mg/dL) 1.01 (0.99~ 1.02) 0.394 TC(Mmol/L) 1.02 (0.93~ 1.12) 0.693 TG(Mmol/L) 1.09 (1.01~ 1.17) 0.018 HDL.C(Mmol/L) 1.06 (0.76~ 1.46) 0.746 LDL.C(Mmol/L) 1.02 (0.91~ 1.14) 0.713 OR, odds ratio; CI, confidence interval; SD, standard deviation Abbreviations as in Table 1 P values in bold are < 0.05 To further clarify the association between glycemia and multi-vessel lesions in patients undergoing percutaneous coronary intervention, we conducted a multivariate logistic analysis. The association between glycemia and coronary artery multi-vessel lesions was significant in the unadjusted model, with each unit increase in glycemia elevating the risk of multi-vessel lesions by 4% (OR: 1.04, P = 0.02). This relationship remained significant after adjusting for factors such as sex, age, smoking, hypertension, diastolic blood pressure, heart rate, uric acid and triglycerides. (Adj. OR: 1.04, P = 0.039) (Table 3). These results indicate that glycemia may be an independent risk factor for the development of coronary artery multi-vessel lesions.After adjusting for various covariates, we observed a linear relationship between glycemia and multi-vessel lesions, with the risk increasing as blood glucose levels rose (Fig. 2). Table3 Multivariate analysis for overall population Variable Model1 Model2 Model3 Model4 n.total n.event_% crude.OR(95%CI) crude.P_value adj.OR(95%CI) adj.P_value 1973 474 (24) 1.04 (1.01~1.08) 0.02 1973 474 (24) 1.04 (1.01~1.08) 0.02 1.04 (1~ 1.07) 0.024 1973 474 (24) 1.04 (1.01~1.08) 0.02 1.04 (1~ 1.07) 0.025 1973 474 (24) 1.04 (1.01~ 1.08) 0.02 1.04 (1~ 1.07) 0.039 Model1：no adjusted Model2：Adj：Model1+Sex+age Model3：Adj：Model2+smoking+hypertension Model4：Adj：Model3+DBP+HR+UA+TG Subgroup Analysis Considering factors like age, gender, and smoking status, revealed more nuanced relationships. The data showed a more pronounced association between glycemia and multi-vessel lesions in men (p=0.031), particularly those aged ≥45 years (p=0.008). This trend was less statistically significant in women (p=0.477) and the younger cohort, aged <45 years (p=0.688). Additionally, smokers demonstrated a stronger correlation (p=0.038) compared to non-smokers (p=0.085). The particular finding was the strong association between a history of previous diabetes and multi-vessel lesions in the univariate analysis (1.85 (1.46-2.34) P < 0.001). While in the subgroup analysis, the association between glycemia and multi-vessel lesions was not significant in diabetic patients, but the association between glycemia and multi-vessel lesions was more significant in non-diabetic patients, we considered that the reason for this result may be due to the fact that diabetic patients took medication to control their blood glucose prior to admission to the hospital, which resulted in low fasting blood glucose values, so that the association between glycemia and multi-vessel lesions was not found to be significant in diabetic patients in the present study. The results of such studies aptly suggest that strict glycemia control is warranted to prevent the development of multi-vessel lesions, regardless of whether a patient has had diabetes mellitus in the past. These variations in the impact of glycemia on multi-vessel lesions across gender, age, and smoking status are illustrated in Figure 3 and Table 4. In summary,the findings underscore the complexity of cardiovascular risk factors and their varied effects across different patient subgroups. Such stratified analysis aids in developing more personalized patient management strategies for those undergoing PCI and highlights the importance of individualized treatment plans considering each patient’s unique characteristics and lifestyle factors. Table4 Subgroup analysis for association between glycemia and multi-vessel lesion Subgroup n.total n.event_% crude.OR_95CI crude.P_value P.for.interaction_1 P.for.interaction_2 Sex Female 632.0 157 (24.8) 1.02 (0.96~ 1.09) 0.477 0.581 0.585 Male Age(years) 1341.0 317 (23.6) 1.05 (1~ 1.09) 0.031 Age＜45 199.0 33 (16.6) 0.98 (0.87~ 1.1) 0.688 0.105 0.208 Age≥45 Smoking 1774.0 441 (24.9) 1.06 (1.02~ 1.1) 0.008 No 1322.0 320 (24.2) 1.03 (1~ 1.07) 0.085 0.258 0.253 Yes DM 651.0 154 (23.7) 1.08 (1~ 1.17) 0.038 Yes 1553.0 333 (21.4) 1.04 (1~ 1.09) 0.074 0.031 0.038 No 420.0 141 (33.6) 0.96 (0.9~1.02) 0.191 OR, odds ratio; CI, confidence interval; SD, standard deviation; Other abbreviations as in Table 1 Discussion Epidemiology and Significance of Multi-Vessel Lesions The increasing prevalence of multi-vessel lesions in clinical practice is a matter of considerable concern. Patients with multibranch vasculopathy are associated with various adverse cardiovascular outcomes [12] .Dziewierz, Siudak et al. have reported that 40%-65% of ST-segment elevation myocardial infarction (STEMI) patients present with multi-vessel lesions or complete coronary occlusions alongside other coronary vasculopathies [13] . Furthermore, Hochman, Sleeper et al. identified multi-vessel lesions as independent predictors of in-hospital mortality in STEMI patients experiencing cardiogenic shock, noted in 60% of PCI cases [14] .This finding is consistent with Sorajja, Bernard J. et al., who observed that tri-vessel disease significantly predicts mortality and reinfarction risks [15] .These findings point to the fact that multi-vessel lesions are a serious and widespread cardiovascular disease process, that the volume of patients who develop multi-vessel lesions is enormous, and emphasise the urgency of clinical understanding and management of multi-vessel lesions. Glycemia and Cardiovascular Disease Glycemia is known to be associated with various cardiovascular conditions [16] . Our study corroborates the established link between elevated glycemia and cardiovascular diseases. We found a significant association between high glycemia and the presence of multi-vessel lesions in patients undergoing PCI. In the present study, our findings are important in two respects. First, there was a significant association between glycemia and coronary mult-ivessel lesion in patients undergoing percutaneous coronary intervention (PCI). This finding is similar to that of Jaekyung Bae et al, who, by including 675 diabetic patients with CAD undergoing PCI, found that intensive glycaemic control (HbA1c level < 6.5%) was strongly associated with improved clinical outcomes after PCI in diabetic patients [17] . This relationship was further confirmed by the findings of Yu Zhang , Haiyan Song et al, who reported that SHR was significantly associated with multivessel CAD risk and predicted CAD severity, and this association was particularly significant in patients with pre-MD and DM [18] . Furthermore, our findings support the study by Luo E, Wang D et al. that TyG index may be a valid predictor of clinical outcome in STEMI patients undergoing PCI [19] . Distinctive Considerations in Asian Populations However, our study further refines these findings. It is particularly noteworthy that our study boasted a larger sample size compared to previous studies and exclusively included an Asian population. This focus is crucial because the lifestyle and genetic background of Asian populations may influence the development of diabetes and cardiovascular disease. This aligns with the WHO expert discussion indicating that Asians are at a considerably higher risk for type 2 diabetes and cardiovascular disease at BMIs below the existing WHO overweight threshold (> or = 25 kg/m2) [20] . Therefore, our study serves as a vital resource for clinical practice. By identifying the association between glycemia and mult-ivessel lesion, physicians can more accurately assess a patient's risk of developing mult-ivessel lesion and develop a personalised treatment plan. Our study, while comprehensive, has its limitations. Ann-Marie Svensson et al.’s study, differing from ours, found an association between both hyperglycemia and hypoglycemia and the 2-year risk of all-cause mortality in patients with acute coronary syndromes. They concluded that the avoidance of both hyperglycemia and hypoglycemia may be equally important in ACS events and emphasized that glycemia regulation remains a crucial target in future randomized trials [21] . Magri CJ, Mintoff D, et al. also demonstrated that hypoglycemia is linked to the development of atherosclerotic disease [22] . Conversely, our study identified a linear relationship between glycemia and mult-ivessel lesion, indicating that the risk of mult-ivessel lesion escalates with increasing glycemia levels. possibly due to the nature of this cross-sectional study. Despite rigorous data screening and statistical adjustment, the influence of potential confounders cannot be entirely eliminated. For example the variability of fasting glycemia values at admission was minimized due to glycemia-lowering medication in both diabetic and nondiabetic patients. Moreover, all participants in this study were patients who underwent percutaneous coronary intervention (PCI), and the incidence of hypoglycemia was low among the study participants, which may limit the generalizability of the findings. Ultimately, this study did not establish a definitive correlation between hypoglycemia and the development of mult-ivessel lesion, highlighting the need for further investigation into this relationship in future studies.However, the study's strengths include its large sample size, rigorous methodology in data collection and analysis, and its focus on a less studied patient demographic. These factors significantly enhance the robustness and relevance of our findings in cardiovascular medicine. The implications of our findings for clinical practice are multifaceted. Given that glycaemic fluctuations significantly affect patient prognosis, strict glycemic control in diabetic patients is vital, not only for diabetes management but also as a strategy to reduce cardiovascular risk. This is particularly crucial in cases of multi-vessel lesions; hence, clinicians should be vigilant about their patients' glycemic status, considering it a key element of cardiovascular risk assessment and management. While our study offers substantial insights, it also paves the way for further research. Longitudinal studies are necessary to understand the long-term effects of glycemic control on cardiovascular outcomes in patients with multi-vessel lesions. Moreover, investigating genetic and lifestyle factors, especially in diverse populations, could provide deeper insight into the relationship between diabetes and cardiovascular disease. Research aimed at developing targeted therapeutic strategies that address both glycemic control and coronary artery disease is also essential. Conclusion In conclusion, our study analyzed the existence of a linear relationship between glycemia and multi-vessel lesion in patients undergoing PCI for coronary artery disease. Even after adjusting for study-related confounders, the results remained significant, demonstrating that the risk of multi-vessel lesions progressively increases with higher glycemia levels. Our study not only contributes new evidence to the understanding of the role of glycemic control in PCI patients but also offers important insights for improved risk assessment and management of cardiovascular disease. These findings hold significant implications for the development of public health policies and the optimization of clinical care, particularly in regions with high prevalence of diabetes and cardiovascular disease, such as Asia. Declarations Ethics approval and consent to participate This study was a cross-sectional study.The study data collection was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, and a waiver of informed consent was also granted. No ethic statement was required for the present research due to the dataset’s public policy statement. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China,( 82174350) Authors' contributions HD and YD conceived and designed the study. LC,and ZL are responsible for data collection RS, QJ, and HC performed the statistical analysis. HD and TT drafted the article. XS and JY performed manuscript revision. All authors read and approved the final manuscript. Acknowledgements We gratefully thank Haimu Yao (Department of Cardiology, The First Affiliated Hospital of Zhengzhou University) for providing the datasets. We are also grateful to Dr. Jie Liu (Department of Vascular and Endovascular Surgery,Chinese PLA General Hospital) and Dr.Wentao Ni(Department of Respiration, Peking University People's Hospital)for their contribution to the statistical support,and study design consultations. Data Availability The data that support the findings of this study are from: Long-term follow-up results in patients undergoing percutaneous coronary intervention (PCI) with drug-eluting stents: results from a single high-volume PCI center [Dataset]. Dryad. https://doi.org/10.5061/dryad.13d31 References Sheth T, Pinilla-Echeverri N, Moreno R, Wang J, Wood DA, Storey RF, Mehran R, Bainey KR, Bossard M, Bangalore S, Schwalm JD, Velianou JL, Valettas N, Sibbald M, Rodés-Cabau J, Ducas J, Cohen EA, Bagai A, Rinfret S, Newby DE, Feldman L, Laster SB, Lang IM, Mills JD, Cairns JA, Mehta SR. Nonculprit Lesion Severity and Outcome of Revascularization in Patients With STEMI and Multivessel Coronary Disease. 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Epub 2010 Jun 18. PMID: 20643243. Hochman JS, Sleeper LA, Webb JG, Sanborn TA, White HD, Talley JD, Buller CE, Jacobs AK, Slater JN, Col J, McKinlay SM, LeJemtel TH. Early revascularization in acute myocardial infarction complicated by cardiogenic shock. SHOCK Investigators. Should We Emergently Revascularize Occluded Coronaries for Cardiogenic Shock. N Engl J Med. 1999;341(9):625 – 34. doi: 10.1056/NEJM199908263410901 . PMID: 10460813. Paul Sorajja, Bernard J. Gersh, David A. Cox, Michael G. McLaughlin, Peter Zimetbaum, Costantino Costantini, Thomas Stuckey, James E. Tcheng, Roxana Mehran, Alexandra J. Lansky, Cindy L. Grines, Gregg W. Stone, Impact of multivessel disease on reperfusion success and clinical outcomes in patients undergoing primary percutaneous coronary intervention for acute myocardial infarction, European Heart Journal, Volume 28, Issue 14, July 2007, Pages 1709–1716, https://doi.org/10.1093/eurheartj/ehm184 Coutinho M, Gerstein HC, Wang Y, Yusuf S. The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years. Diabetes Care. 1999;22(2):233 – 40. doi: 10.2337/diacare.22.2.233 . PMID: 10333939. Bae J, Yoon JH, Lee JH, Nam JH, Lee CH, Son JW, Kim U, Park JS, Shin DG. Long-term effects of the mean hemoglobin A1c levels after percutaneous coronary intervention in patients with diabetes. Korean J Intern Med. 2021;36(6):1365–1376. doi: 10.3904/kjim.2020.694. Epub 2021 Oct 14. PMID: 34645114; PMCID: PMC8588978. Zhang Y, Song H, Bai J, Xiu J, Wu G, Zhang L, Wu Y, Qu Y. Association between the stress hyperglycemia ratio and severity of coronary artery disease under different glucose metabolic states. Cardiovasc Diabetol. 2023;22(1):29. doi: 10.1186/s12933-023-01759-x . PMID: 36755256; PMCID: PMC9909934. .Bae J, Yoon JH, Lee JH, Nam JH, Lee CH, Son JW, Kim U, Park JS, Shin DG. Long-term effects of the mean hemoglobin A1c levels after percutaneous coronary intervention in patients with diabetes. Korean J Intern Med. 2021;36(6):1365–1376. doi: 10.3904/kjim.2020.694. Epub 2021 Oct 14. PMID: 34645114; PMCID: PMC8588978. Luo E, Wang D, Yan G, Qiao Y, Liu B, Hou J, Tang C. High triglyceride-glucose index is associated with poor prognosis in patients with acute ST-elevation myocardial infarction after percutaneous coronary intervention. Cardiovasc Diabetol. 2019;18(1):150. doi: 10.1186/s12933-019-0957-3 . PMID: 31722708; PMCID: PMC6852896. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157 – 63. doi: 10.1016/S0140-6736(03)15268-3. Erratum in: Lancet. 2004;363(9412):902. PMID: 14726171. Svensson AM, McGuire DK, Abrahamsson P, Dellborg M. Association between hyper- and hypoglycaemia and 2 year all-cause mortality risk in diabetic patients with acute coronary events. Eur Heart J. 2005;26(13):1255–61. doi: 10.1093/eurheartj/ehi230 . Epub 2005 Apr 8. PMID: 15821004. Magri CJ, Mintoff D, Camilleri L, Xuereb RG, Galea J, Fava S. Relationship of Hyperglycaemia, Hypoglycaemia, and Glucose Variability to Atherosclerotic Disease in Type 2 Diabetes. J Diabetes Res. 2018;2018:7464320. doi: 10.1155/2018/7464320 . PMID: 30140707; PMCID: PMC6081537. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3893811","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":270194413,"identity":"c47c8467-c220-48e8-85df-7088c44f7ac6","order_by":0,"name":"Hezeng Dong","email":"","orcid":"","institution":"Changchun University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hezeng","middleName":"","lastName":"Dong","suffix":""},{"id":270194414,"identity":"b0f2859c-e6fc-4817-b1a9-8f4f01066bdb","order_by":1,"name":"Zhaozheng Liu","email":"","orcid":"","institution":"The Affiliated Hospital to Changchun University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhaozheng","middleName":"","lastName":"Liu","suffix":""},{"id":270194415,"identity":"b58f9b1f-54fd-44a4-8a7b-5b7858489944","order_by":2,"name":"Hao Chen","email":"","orcid":"","institution":"The Affiliated Hospital to Changchun University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Chen","suffix":""},{"id":270194416,"identity":"b79b45d0-fa99-4111-b014-98bdf0b3761f","order_by":3,"name":"Rui Shi","email":"","orcid":"","institution":"The Affiliated Hospital to Changchun University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Shi","suffix":""},{"id":270194417,"identity":"3235b7b2-8e44-40b7-8deb-33828fee467a","order_by":4,"name":"Qu Jin","email":"","orcid":"","institution":"The Affiliated Hospital to Changchun University of Chinese 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15:57:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79301,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3893811/v1/605ca01d0fed3214ab06c808.jpg"},{"id":50508556,"identity":"bf207c10-89a9-44b0-bcd9-8c155c88d359","added_by":"auto","created_at":"2024-02-01 15:49:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92966,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3893811/v1/d885ab615011554afe3b4415.jpg"},{"id":54266494,"identity":"737b9e05-35c9-4110-82f5-7594949d4e14","added_by":"auto","created_at":"2024-04-08 05:09:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":517570,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3893811/v1/751c70b9-bee6-4ff8-af4b-fb0bffed1cbd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between glycemia and multi-vessel lesion in participants undergoing percutaneous coronary intervention: A cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eAs advancements in coronary interventions continue\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, the evolution of intravascular imaging and functional techniques is notable\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, leading to more frequent diagnosis of multi-vessel lesions. The European Society of Cardiology (ESC) reports that over 50% of patients with ST-Elevation Myocardial Infarction (STEMI) present with concomitant multi-vessel lesions\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. These lesions significantly predict Major Adverse Cardiovascular Events (MACCE), and patients with STEMI and multi-vessel lesions are at increased risk of recurrent cardiovascular events\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Preventing multi-vessel lesions in clinical practice, reducing acute coronary syndromes, mitigating cardiovascular mortality risk, and developing individualised preventative protocols are crucial research areas.\u003c/p\u003e \u003cp\u003eDiabetes mellitus and its complications are widely recognized as significant risk factors for coronary artery disease\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The relationship between glycemia and multi-vessel lesions warrants further investigation. Recent research has shifted focus to glycemic control in diabetic patients undergoing Percutaneous Coronary Intervention (PCI). For example, Joseph B Muhlestein, Jeffrey L Anderson et al. found a marked increase in mortality risk linked to slight rises in fasting glycemia in patients with Coronary Artery Disease (CAD) undergoing PCI, even with hemodialysis. This finding highlights the critical importance of early detection and management of glycemia-related risks\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Additionally, Djupsj\u0026ouml;, Kuhl et al. reported nearly double the long-term cardiovascular mortality and more than twice the incidence of cardiovascular events in patients with hyperglycemia\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Research by A F Zand Parsa et al., involving 125 patients undergoing coronary angiography (group 1), compared to a control group (group 2) matched for age and gender but without proximal lesions, found that proximal and multivessel coronary artery involvement is associated with a history of diabetes, but not with high cholesterol, hypertension, smoking, or hypertriglyceridemia \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.While these studies provide valuable insights, direct evidence correlating glycemia with multi-vessel lesions, particularly in Asian populations, is limited. This gap is especially relevant considering the unique lifestyle and genetic characteristics of Asian populations. Our study aims to address this deficiency by exploring the glycemia-multi-vessel lesion connection in a cross-sectional cohort of patients undergoing PCI. The objective is to provide clinicians with more precise treatment protocols and to enhance the scientific basis for cardiovascular risk management in diabetic patients.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eOur study examined a cohort of patients who underwent Percutaneous Coronary Intervention (PCI) at our institution from July 2009 to August 2011, with a follow-up period tracking long-term outcomes (total n\u0026thinsp;=\u0026thinsp;2533). Selection was based on stringent inclusion criteria. The study encompassed a comprehensive review of coronary angiography records, conforming to the highest clinical standards. After excluding incomplete or ambiguous data, the analysis incorporated 1973 patients' records (Fig.\u0026nbsp;1). The procedural approaches during PCI, including stent selection and use of intravascular ultrasound, were at the discretion of the attending clinicians\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. These choices strictly adhered to current clinical guidelines and best practices, and with the consent of all participants. The primary endpoint was the presence of \u0026ge;\u0026thinsp;50% stenosis in at least two of the three major epicardial vessels\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, ascertained through detailed coronary angiographic assessment using advanced imaging techniques for a precise evaluation of the coronary vasculature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, the comprehensive collection of demographic and clinical data was pivotal to our analysis. We meticulously recorded patient demographics, medical history, smoking status, and other pertinent clinical parameters at admission. These included detailed assessments of smoking history, diabetes mellitus, and hypertension, all defined according to established clinical criteria. Diabetes and hypertension were diagnosed based on standardized clinical and laboratory criteria. Patients were classified as diabetic if their fasting glycemia concentration exceeded 6.1 mmol/L, their glycosylated hemoglobin level surpassed 6.5%, or they were receiving insulin or oral hypoglycemic medications. Hypertension was defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or current use of antihypertensive medication. A smoking history was considered as tobacco use within the preceding ten years. Glycemia values were obtained from fasting blood samples at the time of admission and all other laboratory tests including (Cr,UA,BIL, TC,TG,HDL-C,LDL-C) were also obtained from fasting blood samples at the time of admission. Lesion characteristics (multi-vessel lesions) were determined by performing coronary angiography.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were collected retrospectively using a standardised data collection form. Follow-up information was collected through outpatient, readmission, or telephone contacts and all methods were performed in accordance with the relevant guidelines and regulations\u003csup\u003e[11]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eStudy data were sourced from the dryad database of the First Affiliated Hospital of Zhengzhou University,courtesy of Yao,Hai Mu et al.and are aeeessible via http://doi.org/10.5061/drayd.13d31.The Ethics Committee of the First Affiliated Hospital of Zhengzhou University has endorsed the public policy statement associated with the dataset, eliminating the need for an ethics statement in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, glycemia levels of participants were categorized into four quartiles: Quartile 1 (n = 482), Quartile 2 (n = 500), Quartile 3 (n = 496), and Quartile 4 (n = 495). Participant characteristics were summarized. Categorical variables were presented as numbers (n) and percentages (%), evaluated using chi-square tests. Continuous variables were expressed as mean \u0026plusmn; standard deviation or median (interquartile range) for normally distributed data. We conducted one-way and multifactorial linear regression analyses to examine the relationship between glycemia and multi-vessel lesion. Variables selected based on a p-value \u0026lt;0.05 in univariate analysis, previous literature, or clinical relevance, including age, sex, smoking, hypertension, DBP, HR, UA, and TG, were adjusted for in the multifactorial logistic analysis for the overall population. Subgroup analyses utilized logistic models to ascertain the stability of the glycemia-multi-vessel lesion association across subgroups, including gender, age, and smoking status. All analyses were conducted using Free Statis Approximatics software version 1.9. A two-sided P value \u0026lt;0.05 was deemed statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy Population and Baseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur comprehensive study involved 2,533 patients diagnosed with coronary artery disease undergoing Percutaneous Coronary Intervention (PCI). After rigorous data screening, 1,973 participants were included in the final analysis. The cohort comprised 1,341 men and 632 women, encompassing a wide demographic range. Detailed examination of baseline characteristics identified notable associations between glycemia and several key factors, such as gender, age, hypertension, diabetes mellitus, and the prevalence of multi-vessel lesions (Table1). These insights highlight the complex relationship between metabolic parameters and cardiovascular disease, emphasizing the need for comprehensive patient evaluation and management in clinical practice.\u003c/p\u003e\n\u003cp\u003eTable1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Baseline characteristics of the study participants. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003eAll\u003cbr\u003e\u0026nbsp;participants\u003cbr\u003e\u0026nbsp;(n = 1973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003eQuartile\u003cbr\u003e\u0026nbsp;Glycemia1\u003cbr\u003e\u0026nbsp;(n = 482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003eQuartile\u003cbr\u003e\u0026nbsp;Glycemia2\u003cbr\u003e\u0026nbsp;(n = 500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003eQuartile\u003cbr\u003e\u0026nbsp;Glycemia3\u003cbr\u003e\u0026nbsp;(n = 496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003eQuartile\u003cbr\u003e\u0026nbsp;Glycemia4\u003cbr\u003e\u0026nbsp;(n = 495)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"80\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003esex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e13.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"40\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e632 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e135 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e144 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e168 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e185 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e1341 (68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e347 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e356 (71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e328 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e310 (62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"40\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eAge(years)\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e59.9 \u0026plusmn; 11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e58.7 \u0026plusmn; 12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e59.7 \u0026plusmn; 11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e60.6 \u0026plusmn; 10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e60.8 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e3.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e16.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e975 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e264 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e261 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e238 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e212 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e998 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e218 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e239 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e258 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e283 (57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eDM,\u0026nbsp;\u003cbr\u003e\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e492.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e1553 (78.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e448 (92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e466 (93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e422 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e217 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"40\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e420 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e34 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e34 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e74 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e278 (56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003esmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e7.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"40\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e1322 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e312 (64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e319 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e340 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e351 (70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"40\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e651 (33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e170 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e181 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e156 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e144 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eSBP(mmHg)\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e104.5\u003c/p\u003e\n \u003cp\u003e\u0026plusmn; 28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e108.8 \u0026plusmn; 28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e102.5 \u0026plusmn; 28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e106.6 \u0026plusmn; 29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e100.1 \u0026plusmn; 28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e9.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eDBP(mmHg)\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e77.3\u003c/p\u003e\n \u003cp\u003e\u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e78.0 \u0026plusmn; 11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e76.0 \u0026plusmn; 11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e77.4 \u0026plusmn; 12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e78.0 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e2.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eHeart.rate\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e72.1\u003c/p\u003e\n \u003cp\u003e\u0026plusmn; 11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e69.8 \u0026plusmn;\u0026nbsp;10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e71.1 \u0026plusmn; 10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e73.0 \u0026plusmn; 11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e74.4 \u0026plusmn; 12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e15.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eCr(\u0026mu;mol/L)\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e72.0 \u0026plusmn; 30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e73.3 \u0026plusmn; 25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e72.3 \u0026plusmn; 20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e73.0 \u0026plusmn; 40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e69.2 \u0026plusmn; 31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e1.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eUA(\u0026mu;mol/L)\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e304.2 \u0026plusmn; 92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e306.4 \u0026plusmn; 87.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e308.0 \u0026plusmn; 84.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e310.4 \u0026plusmn; 100.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e291.9 \u0026plusmn; 96.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e4.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"80\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eBIL(mg/dL)\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e9.8 \u0026plusmn; 7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e9.4 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e9.5 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e10.4 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e10.0 \u0026plusmn; 5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e1.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eTC(Mmol/L)\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e4.2 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e4.4 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e9.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eTG(Mmol/L)\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e1.6 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e1.8 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e2.2 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e14.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"60\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eHDL.C(Mmol/L)Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e1.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e1.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e1.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e1.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e1.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e1.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"80\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eLDL.C(Mmol/L)\u0026nbsp;\u003cbr\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e2.8 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e6.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"80\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003emulti.vessel\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e13.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"40\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e1499 (76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e392 (81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e379 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e376 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e352 (71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"40\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.738019169329073%\"\u003e\n \u003cp\u003e474 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e90 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e121 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\"\u003e\n \u003cp\u003e120 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.140575079872205%\"\u003e\n \u003cp\u003e143 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.063897763578275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.223642172523961%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" rowspan=\"2\"\u003e\n \u003cp\u003eData are shown as mean\u0026nbsp;\u0026plusmn; standard deviation (SD) or median (IQR) for continuous variables and proportions (%) for categorical variables\u003cbr\u003e\u0026nbsp;Sex,age,hypertension, DM,smoking,SBP, DBP,heart.rate,Cr, UA, BIL,TC, HDL,C, LDL.C,multi-vessel lesion\u003cbr\u003e\u0026nbsp;P values in bold are \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"76\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate and Multivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn univariate analysis, factors such as age, hypertension, diabetes, blood glucose levels, uric acid levels, and triglycerides significantly correlated with coronary artery multi-vessel lesions. (Table 2).\u003c/p\u003e\n\u003cp\u003eTable2：Univariate analysis for overall population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"460\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003eOR_95CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003eP_value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eSex=female,n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u0026nbsp;(0.75~\u0026nbsp;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u0026nbsp;(1.02~\u0026nbsp;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension,\u003c/p\u003e\n \u003cp\u003en\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.31\u0026nbsp;(1.06~\u0026nbsp;1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eDM,n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.85\u0026nbsp;(1.46~2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking,n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u0026nbsp;(0.78~\u0026nbsp;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eSBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;(1~\u0026nbsp;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eDBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.01\u0026nbsp;(1~\u0026nbsp;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eHeart.rate(Bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.01\u0026nbsp;(1~\u0026nbsp;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eGlycemia(Mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u0026nbsp;(1.01~\u0026nbsp;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eCr(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;(1~\u0026nbsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eUA(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;(1~\u0026nbsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eBIL(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.01\u0026nbsp;(0.99~\u0026nbsp;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eTC(Mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u0026nbsp;(0.93~\u0026nbsp;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eTG(Mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.09\u0026nbsp;(1.01~\u0026nbsp;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eHDL.C(Mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.06\u0026nbsp;(0.76~\u0026nbsp;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.65217391304348%\" valign=\"top\"\u003e\n \u003cp\u003eLDL.C(Mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.47826086956522%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u0026nbsp;(0.91~\u0026nbsp;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.869565217391305%\" valign=\"top\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR, odds\u0026nbsp;ratio;\u0026nbsp;CI, confidence\u0026nbsp;interval; SD, standard\u0026nbsp;deviation\u003c/p\u003e\n\u003cp\u003eAbbreviations as\u0026nbsp;in Table\u0026nbsp;1\u003c/p\u003e\n\u003cp\u003eP values in bold are \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To further clarify the association between glycemia and multi-vessel lesions in patients undergoing percutaneous coronary intervention, we conducted a multivariate logistic analysis. The association between glycemia and coronary artery multi-vessel lesions was significant in the unadjusted model, with each unit increase in glycemia elevating the risk of multi-vessel lesions by 4% (OR: 1.04, P = 0.02). This relationship remained significant after adjusting for factors such as sex, age, smoking, hypertension, diastolic blood pressure, heart rate, uric acid and triglycerides. (Adj. OR: 1.04, P = 0.039) (Table 3). These results indicate that glycemia may be an independent risk factor for the development of coronary artery multi-vessel lesions.After adjusting for various covariates, we observed a linear relationship between glycemia and multi-vessel lesions, with the risk increasing as blood glucose levels rose (Fig. 2).\u003c/p\u003e\n\u003cp\u003eTable3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Multivariate analysis for overall population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.17543859649123%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82456140350877%\" valign=\"top\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.17543859649123%\" valign=\"top\"\u003e\n \u003cp\u003en.total\u003c/p\u003e\n \u003cp\u003en.event_%\u003c/p\u003e\n \u003cp\u003ecrude.OR(95%CI)\u003c/p\u003e\n \u003cp\u003ecrude.P_value\u003c/p\u003e\n \u003cp\u003eadj.OR(95%CI)\u003c/p\u003e\n \u003cp\u003eadj.P_value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.82456140350877%\" valign=\"top\"\u003e\n \u003cp\u003e1973\u003c/p\u003e\n \u003cp\u003e474\u0026nbsp;(24)\u003c/p\u003e\n \u003cp\u003e1.04\u0026nbsp;(1.01~1.08)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1973\u003c/p\u003e\n \u003cp\u003e474\u0026nbsp;(24)\u003c/p\u003e\n \u003cp\u003e1.04\u0026nbsp;(1.01~1.08) 0.02\u003c/p\u003e\n \u003cp\u003e1.04\u0026nbsp;(1~\u0026nbsp;1.07)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1973\u003c/p\u003e\n \u003cp\u003e474\u0026nbsp;(24)\u003c/p\u003e\n \u003cp\u003e1.04\u0026nbsp;(1.01~1.08) 0.02\u003c/p\u003e\n \u003cp\u003e1.04\u0026nbsp;(1~\u0026nbsp;1.07)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e1973\u003c/p\u003e\n \u003cp\u003e474\u0026nbsp;(24)\u003c/p\u003e\n \u003cp\u003e1.04\u0026nbsp;(1.01~\u0026nbsp;1.08) 0.02\u003c/p\u003e\n \u003cp\u003e1.04\u0026nbsp;(1~\u0026nbsp;1.07)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel1：no\u0026nbsp;adjusted\u003c/p\u003e\n\u003cp\u003eModel2：Adj：Model1+Sex+age\u003c/p\u003e\n\u003cp\u003eModel3：Adj：Model2+smoking+hypertension\u003c/p\u003e\n\u003cp\u003eModel4：Adj：Model3+DBP+HR+UA+TG\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering factors like age, gender, and smoking status, revealed more nuanced relationships. The data showed a more pronounced association between glycemia and multi-vessel lesions in men (p=0.031), particularly those aged \u0026ge;45 years (p=0.008). This trend was less statistically significant in women (p=0.477) and the younger cohort, aged \u0026lt;45 years (p=0.688). Additionally, smokers demonstrated a stronger correlation (p=0.038) compared to non-smokers (p=0.085). The particular finding was the strong association between a history of previous diabetes and multi-vessel lesions in the univariate analysis (1.85 (1.46-2.34) P \u0026lt; 0.001). While in the subgroup analysis, the association between glycemia and multi-vessel lesions was not significant in diabetic patients, but the association between glycemia and multi-vessel lesions was more significant in non-diabetic patients, we considered that the reason for this result may be due to the fact that diabetic patients took medication to control their blood glucose prior to admission to the hospital, which resulted in low fasting blood glucose values, so that the association between glycemia and multi-vessel lesions was not found to be significant in diabetic patients in the present study. \u0026nbsp;The results of such studies aptly suggest that strict glycemia control is warranted to prevent the development of multi-vessel lesions, regardless of whether a patient has had diabetes mellitus in the past.\u003c/p\u003e\n\u003cp\u003eThese variations in the impact of glycemia on multi-vessel lesions across gender, age, and smoking status are illustrated in Figure 3 and Table 4.\u003c/p\u003e\n\u003cp\u003eIn summary,the findings underscore the complexity of cardiovascular risk factors and their varied effects across different patient subgroups. Such stratified analysis aids in developing more personalized patient management strategies for those undergoing PCI and highlights the importance of individualized treatment plans considering each patient\u0026rsquo;s unique characteristics and lifestyle factors.\u003c/p\u003e\n\u003cp\u003eTable4 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Subgroup analysis for association between glycemia and multi-vessel lesion\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"656\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003en.total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003en.event_%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003ecrude.OR_95CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003ecrude.P_value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003eP.for.interaction_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003eP.for.interaction_2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e632.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e157\u0026nbsp;(24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u0026nbsp;(0.96~\u0026nbsp;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e1341.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e317\u0026nbsp;(23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u0026nbsp;(1~\u0026nbsp;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eAge＜45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e199.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e33\u0026nbsp;(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u0026nbsp;(0.87~\u0026nbsp;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026ge;45 Smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e1774.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e441\u0026nbsp;(24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e1.06\u0026nbsp;(1.02~\u0026nbsp;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e1322.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e320\u0026nbsp;(24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u0026nbsp;(1~\u0026nbsp;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e651.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e154\u0026nbsp;(23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e1.08\u0026nbsp;(1~\u0026nbsp;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e1553.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e333\u0026nbsp;(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u0026nbsp;(1~\u0026nbsp;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.670731707317072%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.231707317073171%\" valign=\"top\"\u003e\n \u003cp\u003e420.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e141\u0026nbsp;(33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.76829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u0026nbsp;(0.9~1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.902439024390244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR, odds ratio; CI, confidence interval; SD, standard deviation; Other abbreviations as in Table 1\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eEpidemiology and Significance of Multi-Vessel Lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe increasing prevalence of multi-vessel lesions in clinical practice is a matter of considerable concern. Patients with multibranch vasculopathy are associated with various adverse cardiovascular outcomes\u003csup\u003e[12]\u003c/sup\u003e.Dziewierz, Siudak et al. have reported that 40%-65% of ST-segment elevation myocardial infarction (STEMI) patients present with multi-vessel lesions or complete coronary occlusions alongside other coronary vasculopathies\u003csup\u003e[13]\u003c/sup\u003e. Furthermore, Hochman, Sleeper et al. identified multi-vessel lesions as independent predictors of in-hospital mortality in STEMI patients experiencing cardiogenic shock, noted in 60% of PCI cases\u003csup\u003e[14]\u003c/sup\u003e.This finding is consistent with Sorajja, Bernard J. et al., who observed that tri-vessel disease significantly predicts mortality and reinfarction risks\u003csup\u003e[15]\u003c/sup\u003e.These findings point to the fact that multi-vessel lesions are a serious and widespread cardiovascular disease process, that the volume of patients who develop multi-vessel lesions is enormous, and emphasise the urgency of clinical understanding and management of multi-vessel lesions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlycemia and Cardiovascular Disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlycemia is known to be associated with various cardiovascular conditions\u003csup\u003e[16]\u003c/sup\u003e. Our study corroborates the established link between elevated glycemia and cardiovascular diseases. We found a significant association between high glycemia and the presence of multi-vessel lesions in patients undergoing PCI. In the present study, our findings are important in two respects. First, there was a significant association between glycemia and coronary mult-ivessel lesion in patients undergoing percutaneous coronary intervention (PCI). This finding is similar to that of Jaekyung Bae et al, who, by including 675 diabetic patients with CAD undergoing PCI, found that intensive glycaemic control (HbA1c level \u0026lt; 6.5%) was strongly associated with improved clinical outcomes after PCI in diabetic patients \u003csup\u003e[17]\u003c/sup\u003e. This relationship was further confirmed by the findings of Yu Zhang , Haiyan Song et al, who reported that SHR was significantly associated with multivessel CAD risk and predicted CAD severity, and this association was particularly significant in patients with pre-MD and DM \u003csup\u003e[18]\u003c/sup\u003e. Furthermore, our findings support the study by Luo E, Wang D et al. that TyG index may be a valid predictor of clinical outcome in STEMI patients undergoing PCI \u003csup\u003e[19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistinctive Considerations in Asian Populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHowever, our study further refines these findings. It is particularly noteworthy that our study boasted a larger sample size compared to previous studies and exclusively included an Asian population. This focus is crucial because the lifestyle and genetic background of Asian populations may influence the development of diabetes and cardiovascular disease. This aligns with the WHO expert discussion indicating that Asians are at a considerably higher risk for type 2 diabetes and cardiovascular disease at BMIs below the existing WHO overweight threshold (\u0026gt; or = 25 kg/m2)\u003csup\u003e\u0026nbsp;[20]\u003c/sup\u003e. Therefore, our study serves as a vital resource for clinical practice. By identifying the association between glycemia and mult-ivessel lesion, physicians can more accurately assess a patient\u0026apos;s risk of developing mult-ivessel lesion and develop a personalised treatment plan.\u003c/p\u003e\n\u003cp\u003eOur study, while comprehensive, has its limitations. Ann-Marie Svensson et al.\u0026rsquo;s study, differing from ours, found an association between both hyperglycemia and hypoglycemia and the 2-year risk of all-cause mortality in patients with acute coronary syndromes. They concluded that the avoidance of both hyperglycemia and hypoglycemia may be equally important in ACS events and emphasized that glycemia regulation remains a crucial target in future randomized trials\u003csup\u003e[21]\u003c/sup\u003e. Magri CJ, Mintoff D, et al. also demonstrated that hypoglycemia is linked to the development of atherosclerotic disease\u003csup\u003e[22]\u003c/sup\u003e. Conversely, our study identified a linear relationship between glycemia and mult-ivessel lesion, indicating that the risk of mult-ivessel lesion escalates with increasing glycemia levels. possibly due to the nature of this cross-sectional study. Despite rigorous data screening and statistical adjustment, the influence of potential confounders cannot be entirely eliminated. For example the variability of fasting glycemia values at admission was minimized due to glycemia-lowering medication in both diabetic and nondiabetic patients. Moreover, all participants in this study were patients who underwent percutaneous coronary intervention (PCI), and the incidence of hypoglycemia was low among the study participants, which may limit the generalizability of the findings. Ultimately, this study did not establish a definitive correlation between hypoglycemia and the development of mult-ivessel lesion, highlighting the need for further investigation into this relationship in future studies.However, the study\u0026apos;s strengths include its large sample size, rigorous methodology in data collection and analysis, and its focus on a less studied patient demographic. These factors significantly enhance the robustness and relevance of our findings in cardiovascular medicine.\u003c/p\u003e\n\u003cp\u003eThe implications of our findings for clinical practice are multifaceted. Given that glycaemic fluctuations significantly affect patient prognosis, strict glycemic control in diabetic patients is vital, not only for diabetes management but also as a strategy to reduce cardiovascular risk. This is particularly crucial in cases of multi-vessel lesions; hence, clinicians should be vigilant about their patients\u0026apos; glycemic status, considering it a key element of cardiovascular risk assessment and management. While our study offers substantial insights, it also paves the way for further research. Longitudinal studies are necessary to understand the long-term effects of glycemic control on cardiovascular outcomes in patients with multi-vessel lesions. Moreover, investigating genetic and lifestyle factors, especially in diverse populations, could provide deeper insight into the relationship between diabetes and cardiovascular disease. Research aimed at developing targeted therapeutic strategies that address both glycemic control and coronary artery disease is also essential.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study analyzed the existence of a linear relationship between glycemia and multi-vessel lesion in patients undergoing PCI for coronary artery disease. Even after adjusting for study-related confounders, the results remained significant, demonstrating that the risk of multi-vessel lesions progressively increases with higher glycemia levels. Our study not only contributes new evidence to the understanding of the role of glycemic control in PCI patients but also offers important insights for improved risk assessment and management of cardiovascular disease. These findings hold significant implications for the development of public health policies and the optimization of clinical care, particularly in regions with high prevalence of diabetes and cardiovascular disease, such as Asia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a cross-sectional study.The study data collection was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, and a waiver of informed consent was also granted. No ethic statement was required for the present research due to the dataset\u0026rsquo;s public policy statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China,( 82174350)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHD and YD conceived and designed the study. LC,and ZL are responsible for data collection RS, QJ, and HC performed the statistical analysis. HD and TT drafted the article. XS and JY performed manuscript revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully thank Haimu Yao (Department of Cardiology, The First Affiliated Hospital of Zhengzhou University) for providing the datasets. We are also grateful to Dr. Jie Liu (Department\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eof Vascular and Endovascular Surgery,Chinese PLA General Hospital) and Dr.Wentao Ni(Department of Respiration, Peking University People\u0026apos;s Hospital)for their contribution to the statistical support,and study design consultations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are from: Long-term follow-up results in patients undergoing percutaneous coronary intervention (PCI) with drug-eluting stents: results from a single high-volume PCI center [Dataset]. Dryad. https://doi.org/10.5061/dryad.13d31\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSheth T, Pinilla-Echeverri N, Moreno R, Wang J, Wood DA, Storey RF, Mehran R, Bainey KR, Bossard M, Bangalore S, Schwalm JD, Velianou JL, Valettas N, Sibbald M, Rod\u0026eacute;s-Cabau J, Ducas J, Cohen EA, Bagai A, Rinfret S, Newby DE, Feldman L, Laster SB, Lang IM, Mills JD, Cairns JA, Mehta SR. Nonculprit Lesion Severity and Outcome of Revascularization in Patients With STEMI and Multivessel Coronary Disease. J Am Coll Cardiol. 2020;76(11):1277\u0026ndash;1286. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2020.07.034\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2020.07.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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PMID: 30140707; PMCID: PMC6081537.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glycemia, Multi-Vessel Lesion, Percutaneous Coronary Intervention","lastPublishedDoi":"10.21203/rs.3.rs-3893811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3893811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aims to elucidate the association between glycemia and the occurrence of multi-vessel lesions in participants undergoing Percutaneous Coronary Intervention (PCI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cohort of 2,533 patients with coronary heart disease, treated with drug-eluting stents, was analysed. Of these, 1,973 patients, identified by the endpoint of multi-vessel lesions, were examined using univariate and multivariate logistic regression analyses to determine the relationship between glycemia levels and multi-vessel lesion occurrence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analysis included 1,973 participants, among whom 474 patients were identified with coronary multi-vessel lesions. Univariate logistic regression analysis demonstrated a positive correlation between glycemia and the occurrence of coronary multi-vessel lesions (OR 1.04; 95% CI 1.01\u0026ndash;1.08; p\u0026thinsp;=\u0026thinsp;0.02)..The adjusted model indicated that for each unit increase in glycemia, the risk of developing coronary multi-vessel lesions increased by 4%, showing a significant correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subgroup analyses revealed that the impact of glycemia on multi-vessel lesions in patients with PCI varied according to gender, age, and smoking status, with the effect being more pronounced in men, older patients, and smokers。\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings establish a significant association between glycemia and the incidence of multi-vessel lesions, particularly pronounced in male patients, individuals over 45, and smokers.\u003c/p\u003e","manuscriptTitle":"Association between glycemia and multi-vessel lesion in participants undergoing percutaneous coronary intervention: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 15:49:54","doi":"10.21203/rs.3.rs-3893811/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae76516e-ad67-4402-a3c1-c13fa4766615","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28475839,"name":"Health sciences/Cardiology"},{"id":28475840,"name":"Health sciences/Endocrinology"},{"id":28475841,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-04-08T05:01:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-01 15:49:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3893811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3893811","identity":"rs-3893811","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}