Joint association of TyG index and LDL-C with all-cause and cardiovascular mortality among patients with cardio-renal-metabolic disease

Joint association of TyG index and LDL-C with all-cause and cardiovascular mortality among patients with cardio-renal-metabolic disease | 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 Joint association of TyG index and LDL-C with all-cause and cardiovascular mortality among patients with cardio-renal-metabolic disease Wenguang Lai, Yucui Lin, Zhiyong Gao, Zhidong Huang, Tingting Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4890377/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Background: Both the triglyceride-glucose (TyG) index, as a surrogate marker of insulin resistance, and low-density lipoprotein cholesterol (LDL-C) are independent risk factors for long-term prognosis among patients with cardio-renal-metabolic (CRM) disease. However, the co-exposures of TyG index and LDL-C to all-cause and cardiovascular death among patients with CRM remain unknown. Methods: Patients with CRM from the National Health and Nutrition Examination Survey (NHANES) database (1999–2018) were included. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Multivariable Cox and restricted cubic spline (RCS) regression models were used to estimate the individual and joint association of TyG index and LDL-C with the risk of all-cause and cardiovascular mortality. The interaction between the TyG index and LDL-C to mortality was also evaluated. Results: During a median follow-up of 7.6 years, there were 1608 (22.8%) and 609 (8.4%) patients who died from all-cause and cardiovascular mortality, respectively. In patients with LDL-C<2.6 mmol/L, no significant differences were observed in all-cause and cardiovascular mortality when comparing higher TyG index to the lowest tertile (T1). Specifically, the hazard ratios (HRs) for all-cause mortality in the second (T2) and third tertiles (T3) were 0.81 (95%CI: 0.59–1.09) and 0.87 (95%CI: 0.62–1.22), respectively, with a P for trend of 0.468. For cardiovascular mortality, the HRs for T2 and T3 compared to T1 were 0.80 (95%CI: 0.48–1.32) and 0.72 (95%CI: 0.45–1.15), respectively, with a P for trend of 0.173. However, elevated TyG index was related to markedly increased risk of all-cause and cardiovascular mortality in patients with LDL-C≥2.6 mmol/L. Specifically, for all-cause mortality, HRs for T2 and T3 compared to T1 were 1.01 (95%CI: 0.79–1.28) and 1.38 (95%CI: 1.07–1.79), respectively, with a P for trend of 0.009. For cardiovascular mortality, the HRs were 1.09 (95% CI: 0.72–1.65) for T2 and 1.80 (95% CI: 1.18–2.75) for T3, with a P for trend of 0.005. Similar results were found in RCS and sensitivity analyses. Interactive analysis also demonstrated that a significant association of TyG index and LDL-C with the risk of all-cause (P for interaction=0.011) and cardiovascular (P for interaction=0.050) mortality was observed. Conclusions: Our research demonstrated the co-exposure effects between the TyG index and LDL-C on all-cause and cardiovascular mortality. Elevated TyG index can significantly increase the risk of all-cause and cardiovascular mortality only among CRM patients with LDL-C ≥ 2.6 mmol/L, but not among patients with LDL-C < 2.6 mmol/L. Health sciences/Risk factors Health sciences/Endocrinology Health sciences/Endocrinology/Endocrine system and metabolic diseases/Dyslipidaemias Health sciences/Biomarkers/Prognostic markers Health sciences/Diseases/Endocrine system and metabolic diseases Cardio-renal-metabolic disease All-cause mortality Cardiovascular mortality Low-density lipoprotein cholesterol Triglyceride-glucose index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Cardiac, renal, and metabolic (CRM) conditions remain a leading cause of morbidity and mortality in the United States, which is estimated to account for about a third of contemporary deaths[1]. Cardiovascular, kidney, and metabolic functions are closely linked[2], and there are common pathophysiological and risk factors with the onset of the diseases[3]. The impairment in one system may promote and amplify dysfunction in the others, leading to subsequent morbidity and mortality[4-6]. Therefore, the joint management of risk factors for the risk assessment and improvement of the prognosis among CRM patients is crucial. Insulin resistance (IR), characterized by reduced sensitivity or responsiveness to insulin metabolism, has been widely recognized as an independent risk factor for death among CRM patients [3]. The triglyceride–glucose index (TyG) has been considered a dependable and surrogate biomarker for reflecting IR[7]. Current research has clearly established a strong association between the TyG index and mortality within high-risk population, such as those with acute myocardial infarction (AMI)[8], acute heart failure (AHF)[9], and patients in intensive care units (ICU)[10]. The prognostic potential of the TyG index is also being increasingly explored among population at moderate to low risk, including those with chronic kidney disease(CKD)[11], diabetes (DM)[12], and the general population[13]. However, the relationships in these population remain unclear. Further stratification and detailed analysis may be necessary to clarify these associations. Low-density lipoprotein cholesterol (LDL-C) can permeate arterial walls, accumulate within the intima, and promote the generation of atherosclerotic plaque, which can significantly constitute a major risk factor for long-term mortality of CRM patients[14, 15]. Previous studies have shown that joint assessment and management of the TyG index with other risk factors is essential for risk stratification and improvement of prognosis in the community population. Cui et al found the joint assessment of TyG index and inflammation contributes to residual risk stratification and primary prevention of cardiovascular disease (CVD)[16]. A growing body of research found that TyG index demonstrated a strong association with dyslipidemia, which displayed superior risk stratification for the long-term prognosis[17, 18]. Therefore, there is potential for a significant interaction between the TyG index and LDL-C in relation to long-term mortality, however, the precise nature of this interaction is currently not well understood in the CRM population. Therefore, the objection of this research was to investigate the joint effect and risk reclassification of TyG index and LDL-C with all-cause and cardiovascular mortality, based on a large-scale prospective cohort, which may be beneficial to the clinical management of CRM patients in clinical practice. Methods Study design and population We employed data from the National Health and Nutrition Examination Survey (NHANES) database, a program administered by the Centers for Disease Control and Prevention and the National Centers for Health Statistics in the US. CRM disease is a constellation of conditions that includes CVD, CKD, as well as DM[19], and patients with CRM disease were included in this research. The Research Ethics Review Committee of the National Center for Health Statistics has approved the NHANES study, and all participants provided written informed consent. The cohort included a total of 21,604 patients with CRM during 1999–2018. Patients meeting the following criteria were excluded: (1) participants with age <18; (2) receiving dialysis treatment or combining with renal failure (estimated glomerular filtration rate (eGFR)＜15 ml/min/1.73m 2 ) in the past year; (3) combined with malignant tumor; (4) participants experiencing pregnancy; (5) missing information on TyG index (fasting blood glucose [FBG] or triglyceride [TG]), or LDL-C; (6) missing information on mortality or survival time. Finally, a total of 6,010 eligible patients with CRM were enrolled in the study (Figure 1). Outcomes and exposure definitions The main outcomes of this research were all-cause and cardiovascular mortality. Information about mortality status and the cause of death was gained from the NHANES Linked Mortality File, which was created by the National Center for Health Statistics (NCHS) by linking the NHANES data to the National Death Index (NDI). NHANES used an autoanalyzer to enzymatically measure plasma TG, FPG, and LDL-C levels from fasting blood samples. The TyG index was calculated by Ln [TG (mg/dL) × FPG (mg/dl)/2]. Definition of covariates Baseline variables, including demographic data (age, gender, educational levels, race, insurance, poverty index, and matrimony), lifestyle variables (smoking status, alcohol drinking, and physical activity), medical history(CVD, anemia, hypertension[HT], CKD, DM), drug history(glucose-lowering drugs, antihypertensive drugs, as well as lipid-lowering drugs), anthropometric measurements(height, weight, body mass index[BMI], systolic blood pressure[SBP], and diastolic blood pressure[DBP]), and laboratory variables (hemoglobin A1c [HbA1c], high-density lipoprotein cholesterol[HDL-C], neutrophil/lymphocyte ratio[NLR], total cholesterol, white blood cell[WBC], blood urea nitrogen[BUN], serum creatinine, serum uric acid, eGFR, and urinary albumin/creatinine ratio[uACR]) were selected. Demographics, lifestyle variables, medical history, and drug history were collected through self-reported questionnaires. Anthropometric indicators and biochemical parameters were obtained through medical examinations and laboratory tests, respectively. Further details can be found at https://www.cdc.gov/nchs/nhanes/index.htm. Variables including smoking (current smoker, former smoker, and never smoked), alcohol drinking (none, moderate, and heavy), education (below high school, high school, and college or above), matrimony (never married, divorced/separated/widowed, and married/living with partner) and physical activity (PA) (moderate, sedentary, and vigorous) were further defined. CVD is composed of heart attack, congestive heart failure, angina, CAD, and stroke[20]. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation[21], and CKD was defined as eGFR ≤ 60 ml/min/1.73 m2 or uACR ≥30 mg/g. Diabetes was defined as fasting glucose ≥ 7.0 mmol/L or HbA1c(%)≥6.5 or self-reported diagnosis history of diabetes or use of any hypoglycemic medication[22]. Anemia is defined as men with a hemoglobin level less than 130 g/L and women with a hemoglobin level less than 120 g/L according to World Health Organization standards[23]. Statistical analysis All statistical analyses were conducted using a complex, multistage probability sampling design. The study includes data from eight distinct survey cycles over a period of 18 years, starting with the initial phase from 1999 to 2002 and continuing with biennial cycles from 2003 to 2018. Fasting sample weights were applied in accordance with the NHANES Analytical Guidelines. Baseline characteristics between groups were compared using weighted analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Restricted cubic splines (RCS) analysis was utilized to assess the dose-response relationships of the TyG index with all-cause and cardiovascular mortality in LDL-C < 2.6 mmol/L and ≥ 2.6 mmol/L, respectively. Kaplan-Meier method was applied to time-to-event endpoints, with a stratified log-rank test used to compare differences among groups defined by TyG and LDL-C levels. Cox regression analysis was used to evaluate the individual and joint effects of the TyG index and LDL-C on cardiovascular and all-cause mortality. To evaluate whether there is a linear trend in mortality risk across increasing exposure groups, we transformed the categorical exposure groups into a numerical scale. This transformation served as the primary predictor in our model. The estimated coefficient for this variable quantifies the trend in mortality risk corresponding to escalating levels of exposure. To explore the potential interaction between TyG index and LDL-C in relation to mortality, an interaction term (TyG * LDL-C) was introduced into the model. The significance of the interaction was assessed using a likelihood ratio test, comparing the fit of the model with and without the interaction term. Subgroup analyses were conducted, stratified by the presence of CVD, CKD, and DM. Missing covariates were handled using multiple imputations, and a sensitivity analysis was performed. A list of missing covariates is provided in Supplementary Table 1. The analyses were performed using R software (Version 4.2.3), and statistical significance was determined using two-tailed tests with a threshold of P<0.05. Results Baseline characteristics We included 6,010 CRM patients in the final analysis. The mean age of the patients was 56.96 ± 0.31 years, with females accounting for approximately 52.97%. Table 1 presents the baseline characteristics according to LDL-C and TyG tertiles. Patients with elevated TyG index tend to be male and often have comorbidities like anemia, CKD, and DM. They show higher levels of BMI, SBP, DBP, WBC, HbA1c, and uric acid. Moreover, these patients are more likely to receive glucose-lowering drugs, antihypertensive drugs, and lipid-lowering drugs, irrespective of their LDL-C levels being < 2.6 mmol/L or ≥2.6 mmol/L. While patients with higher TyG index were more likely to be drinking; and combined HT in the LDL-C＜2.6 mmol/L group; and have a higher level of uACR in the LDL-C ≥ 2.6 mmol/L group (Table 1). With a median follow-up period was 7.6 years ([interquartile range: 4.1–11.9]), there were 1370 (22.8%) and 504 (8.4%) patients who died from all-cause and cardiovascular death, respectively. Participants with elevated TyG index have higher all-cause mortality (P for trend=0.001) and cardiovascular mortality (P for trend=0.008) among patients with LDL-C ≥ 2.6 mmol/L. However, an increase in long-term all-cause mortality (P for trend=0.351) and cardiovascular mortality (P for trend=0.540) with higher TyG index were not statistically significant for LDL-C < 2.6 mmol/L (Figure 2). Further analysis revealed that among participants with LDL-C < 2.6 mmol/L, an increase in the TyG index was associated with an even decreasing trend in short-term mortality, including both all-cause mortality (P for trend = 0.010) and cardiovascular mortality (P for trend = 0.020) (Supplementary Figure 1). Survival curve analysis consistently demonstrated that the association between the TyG index and the risk of all-cause (P = 0.003) and cardiovascular (P = 0.010) mortality varied according to LDL-C stratification (Supplementary Figure 2). The association of TyG index, and LDL-C with all-cause and cardiovascular mortality After adjusting for confounders, neither individual TyG index nor LDL-C were independently associated with all-cause or cardiovascular mortality (Supplementary Table 2). However, the combined analysis revealed significant associations between categories of LDL-C and TyG index and long-term mortality. Among patients with LDL-C≥2.6 mmol/L, increasing TyG index was linked to higher risks of all-cause and cardiovascular mortality. Specifically, compared to the first tertile (T1), the hazard ratios (HRs) for the second (T2) and third tertiles (T3) were 1.01 (95%CI: 0.79–1.28) and 1.38 (95%CI: 1.07–1.79) for all-cause mortality, and 1.09 (95%CI: 0.72–1.65) and 1.80 (95%CI: 1.18–2.75) for cardiovascular mortality, respectively, with a P for trend of 0.009 for all-cause mortality and 0.005 for cardiovascular mortality. However, there was no statistically significant association between TyG index and all-cause or cardiovascular mortality in patients with LDL-C < 2.6 mmol/L. Specifically, the HRs for all-cause mortality for T2 and T3 compared to T1 were 0.81 (95%CI: 0.59–1.09) and 0.87 (95%CI: 0.62–1.22), respectively, with a P for trend of 0.468. For cardiovascular mortality, the HRs were 0.80 (95%CI: 0.48–1.32) and 0.72 (95%CI: 0.45–1.15), respectively, with a P for trend of 0.173. Interactive analyses further revealed significant interactions between TyG index and LDL-C in predicting risks for all-cause (P for interaction = 0.011) and cardiovascular mortality (P for interaction = 0.050) (Figure 3). Further analysis also found that compared with patients with LDL-C ≥2.6 mmol/L and TyG in T1, those with LDL-C ≥ 2.6 mmol/L and TyG in T3 showed increased risks of all-cause mortality (HR: 1.47, 95%CI: 1.15–1.88, P=0.002) and cardiovascular mortality (HR: 1.65, 95%CI: 1.09–2.49, P=0.018). Similarly, patients with LDL-C < 2.6 mmol/L and TyG in T1 also exhibited elevated risks for all-cause mortality (HR: 1.59, 95%CI: 1.22–2.08, P<0.001) and cardiovascular mortality (HR: 1.64, 95% CI: 1.04–2.58, P=0.032)(Table 2). Further subgroup analysis also showed a similar trend (Figure 4). The results also remained consistent in analyses conducted on datasets after the imputation of missing data (Supplementary Table 3 and Supplementary Figure 3). Restricted cubic spline analyses also demonstrated a nearly linear relationship between the continuous scale TyG and both all-cause mortality (nonlinear P=0.182) and cardiovascular mortality (nonlinear P=0.244) in patients with LDL-C≥2.6 mmol/L, Conversely, no significant relationship was observed between TyG index and either all-cause mortality and or cardiovascular mortality among patients with LDL-C＜2.6 mmol/L(Figure 5). Discussion To our knowledge, this is the first prospective study that evaluated the joint association of TyG index and LDL-C levels with the subsequent risk of all-cause and cardiovascular mortality among CRM patients. Our research found the associations of TyG index with all-cause and cardiovascular mortality were modified by LDL-C levels. Elevated TyG index can significantly increase the risk of all-cause and cardiovascular mortality only among CRM patients with LDL-C ≥ 2.6 mmol/L, but not among patients with LDL-C < 2.6 mmol/L. Further analysis also observed a significant interaction. Therefore, joint assessment of TyG index and LDL-C should be emphasized for risk stratification and improvement of long-term prognosis among CRM patients in the community. Previous research has found that individual TyG index is significantly associated with the risk of mortality in emergency and critically ill population. A higher baseline TyG index significantly increases the risk of mortality among critically ill patients[24-26]. Zheng et al. also found that baseline TyG is related to the prognosis of cardiac arrest[27]. Further research also found the dynamic changes in TyG during hospitalization were related to one-year all-cause death among critically ill patients with CAD[28]. Therefore, the TyG index has important prognostic significance among the critically ill population in clinical practice. However, in the general or community population, the effect of individual TyG index on long-term prognosis was weak, even without statistical difference. Chen et al. demonstrated that after full adjustment, a significant relationship between the TyG stratification and all-cause and cardiovascular death in the general population was not observed [29]. Ghazaal et al. consistently found that after adjusting for DM, the effects of TyG index on all-cause and cardiovascular death disappeared, and TyG index was not associated with the risk of non-cardiovascular death in the general population[30]. Our study also found that individual TyG index was not associated with all-cause and cardiovascular death among CRM subjects in the community cohort. Therefore, further studies are needed to explore risk reclassification in the general community population. Previous research showed that TyG index was positively related to lipid-related markers by Pearson correlation analysis, including LDL-C (r=0.238, P<0.001) in AMI patients without DM[28]. However, there has been no research to explore the joint association of TyG index and LDL-C with the risk of long-term mortality among CRM patients in the community. Our study found that there is a significant joint association of TyG index and LDL-C with all-cause and cardiovascular mortality among CRM patients, which is no difference between all-cause and cardiovascular mortality on an elevated TyG index in patients with LDL-C < 2.6 mmol/L, while higher TyG index was related to significantly increased risk of all-cause and cardiovascular mortality among patients with LDL-C ≥ 2.6 mmol/L. The underlying mechanisms of the joint effect of TyG index and LDL-C remain unknown. The TyG index is significantly associated with long-term prognosis and has been recognized as a reliable and newfound biomarker for gauging IR[7]. Pathophysiological research has indicated IR can induce low-grade inflammation, dyslipidemia, and chronic hyperglycemia, which may serve as important mechanisms for the increased risk of death[31-33]. Moreover, IR induces dyslipidemia, mainly manifested as an increase in serum total cholesterol, LDL-C, or triglycerides, together increasing the mortality of all-cause and cardiovascular death[34]. Furthermore, the accumulation of triglyceride and LDL-C in hepatocytes will further promote IR through inflammation, oxidative stress, and lipotoxicity[35]. Therefore, the joint effect of IR with dyslipidemia may significantly increase the risk of all-cause and cardiovascular mortality. Interestingly, findings from our studies suggested an association of low LDL-C levels with a higher risk of all-cause and cardiovascular death among CRM patients, and subgroup analysis also showed a consistent trend. A growing body of research has also shown a similar relationship. Previous research indicated a paradoxical relationship between low LDL-C levels and a higher risk of death among patients with acute coronary syndrome[36, 37]. In addition, a recent meta-analysis demonstrated that the benefits of lowering LDL-C did not exist when the LDL-C < 2.6 mmol/L[38]. Consistently, Wu et al, also found low LDL-C levels increased the risk of all-cause and cause-specific death[39]. A plausible cause for the lack of a significant correlation between LDL-C and long-term prognosis is that CRM subjects with low LDL-C levels were in poor condition and had more complications. Our research also showed that patients with low LDL-C levels had a higher prevalence of CVD-related diseases, as well as other complications related to poor prognosis, including anemia (12.94% vs. 7.04%), HT (59.99% vs. 51.48%), and poor renal function. Similarly, Kovesdy et al. also found that the paradoxical association of low LDL-C levels with a higher risk of mortality attenuated as the adjusted variables increased. After further correcting for confounders, low LDL-C levels were no longer independently associated with an increased risk of death[40]. Therefore, the higher all-cause and cardiovascular death among participants with low LDL-C levels may be caused by underlying disease to some extent. Understanding the joint associations of TyG index and LDL-C levels with all-cause and cardiovascular death and their predictive values in long-term prognosis could help identify patients at high risk of poor prognosis, and enhance the understanding of pathophysiological mechanisms. Hence, the joint evaluation of TyG and LDL-C in routine clinical practice is essential to improve prognosis among patients with CRM. Besides, our results highlighted the importance of regular detection of TyG index and LDL-C may reduce the risk of all-cause and cardiovascular death with the most appropriate interventions as soon as possible. In addition, excessively low LDL-C levels in the assessment of the risk of long-term death may serve as a newfound indicator in clinical practice, and if necessary, screen for related comorbidities that may affect death. Ultimately, further high-quality prospective research for the joint effect between TyG index and LDL-C in CRM patients should be conducted to evaluate the appropriate management strategy. Limitations several limitations need to be addressed. Firstly, despite adjusting for potential confounders, there are still residual confounding effects of indefinite factors that may contribute to the increased risk of death. Second, this study only explored the baseline TyG index, and did not evaluate how the temporal variation of this index affects its association with death. Tian et al. also have shown that different periods of cumulative TyG lead to different risks of mortality[41]. Thirdly, due to sample size limitations, we did not further evaluate the relationship between TyG index and mortality, based on the cutoff of LDL-C<1.8 mmol/L. However, The National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) guidelines recommends 2.6 mmol/L as an appropriate cutoff in a community cohort[42]. Finally, the NHANES study was conducted only in the United States, and it is unclear whether our findings are applicable to other regions. Therefore, further high-quality studies are needed to verify our findings. Conclusion Our research found the relationship between TyG index and all-cause and cardiovascular mortality was modified by LDL-C levels. Elevated TyG index can significantly increase the risk of all-cause and cardiovascular mortality only among CRM patients with LDL-C ≥ 2.6 mmol/L, but not among patients with LDL-C < 2.6 mmol/L. Joint assessment of TyG index and LDL-C levels should be emphasized for risk stratification and improvement of long-term prognosis among CRM patients. Declarations Acknowledgement We extend our heartfelt gratitude to all participants and investigators involved in the NHANES study. Your invaluable contributions have been instrumental to our research endeavors. Ethics approval and consent to participate. Informed consent has been obtained from every participant and therefore there was no need for any ethical consent in this study. The NCHS ethics review board has approved the NHANES protocol. All procedures were performed in accordance with the relevant guidelines and regulations. Conflict of Interest Statement The authors declared no competing interests for this work. Funding Sources This research was funded and supported by Guangdong Medical Research Foundation (A2024142). Author Contributions The authors’ responsibilities were as follows—(I) Research idea and study design: Wenguang Lai, Yucui Lin. (Ⅱ) Statistical analysis: Wenguang Lai, Zhidong Huang; (Ⅲ) Supervision and mentorship: Tingting Zhang; (Ⅳ) revised the manuscript: Zhiyong Gao. Data Availability Statement All data are available as publicly accessible datasets through NHANES. It is open and publicly accessible through the following link: https://wwwn.cdc.gov/nchs/nhanes/. Reference Ahmad, F.B. and R.N. Anderson, The Leading Causes of Death in the US for 2020. Jama, 2021. 325 (18): p. 1829-1830. Sarafidis, P., et al., SGLT-2 inhibitors and GLP-1 receptor agonists for nephroprotection and cardioprotection in patients with diabetes mellitus and chronic kidney disease. A consensus statement by the EURECA-m and the DIABESITY working groups of the ERA-EDTA. Nephrol Dial Transplant, 2019. 34 (2): p. 208-230. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol, 2014. 2 (8): p. 634-47. Cherney, D.Z.I., et al., Impact of Cardio-Renal-Metabolic Comorbidities on Cardiovascular Outcomes and Mortality in Type 2 Diabetes Mellitus. Am J Nephrol, 2020. 51 (1): p. 74-82. Forman, D.E., et al., Multimorbidity in Older Adults With Cardiovascular Disease. J Am Coll Cardiol, 2018. 71 (19): p. 2149-2161. Thomas, G., et al., Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol, 2011. 6 (10): p. 2364-73. Tahapary, D.L., et al., Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr, 2022. 16 (8): p. 102581. Drwiła, D., et al., Prognostic value of the triglyceride-glucose index among non-diabetic patients with acute myocardial infarction at one-year follow-up. Kardiol Pol, 2021. 79 (10): p. 1116-1123. Cheng, H., et al., The triglyceride glucose index predicts short-term mortality in non-diabetic patients with acute heart failure. Adv Clin Exp Med, 2024. 33 (2): p. 103-110. Liao, Y., et al., Triglyceride-glucose index linked to all-cause mortality in critically ill patients: a cohort of 3026 patients. Cardiovasc Diabetol, 2022. 21 (1): p. 128. Qin, Y., et al., Triglyceride-glucose index and mortality risk in individuals with or without chronic kidney disease: Insights from a national survey of United States adults, 1999-2018. Nutr Metab Cardiovasc Dis, 2024. 34 (8): p. 1994-2001. Liu, C., et al., Association between the triglyceride-glucose index and all-cause and CVD mortality in the young population with diabetes. Cardiovasc Diabetol, 2024. 23 (1): p. 171. Chen, J., et al., Association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population. Cardiovasc Diabetol, 2023. 22 (1): p. 320. Valensi, P., et al., The impact of diabetes on heart failure development: The cardio-renal-metabolic connection. Diabetes Res Clin Pract, 2021. 175 : p. 108831. Whaley-Connell, A. and J.R. Sowers, Basic science: Pathophysiology: the cardiorenal metabolic syndrome. J Am Soc Hypertens, 2014. 8 (8): p. 604-6. Cui, C., et al., Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study. Cardiovasc Diabetol, 2024. 23 (1): p. 156. Wen, J., et al., Association of triglyceride-glucose index with atherosclerotic cardiovascular disease and mortality among familial hypercholesterolemia patients. Diabetol Metab Syndr, 2023. 15 (1): p. 39. Zhao, K., et al., Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio predict the prognosis in patients with type B aortic dissection receiving thoracic endovascular aortic repair. J Thorac Dis, 2024. 16 (3): p. 1971-1983. Ostrominski, J.W., et al., Prevalence and Overlap of Cardiac, Renal, and Metabolic Conditions in US Adults, 1999-2020. JAMA Cardiol, 2023. 8 (11): p. 1050-1060. Zhang, Y., et al., Exposure to acrylamide and the risk of cardiovascular diseases in the National Health and Nutrition Examination Survey 2003-2006. Environ Int, 2018. 117 : p. 154-163. Levey, A.S., et al., A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med, 1999. 130 (6): p. 461-70. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024. Diabetes Care, 2024. 47 (Suppl 1): p. S20-s42. Seitz, A.E., M.S. Eberhardt, and S.L. Lukacs, Anemia Prevalence and Trends in Adults Aged 65 and Older: U.S. National Health and Nutrition Examination Survey: 2001-2004 to 2013-2016. J Am Geriatr Soc, 2018. 66 (12): p. 2431-2432. Cai, W., et al., Association between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database. Cardiovasc Diabetol, 2023. 22 (1): p. 138. Zhang, R., et al., Independent effects of the triglyceride-glucose index on all-cause mortality in critically ill patients with coronary heart disease: analysis of the MIMIC-III database. Cardiovasc Diabetol, 2023. 22 (1): p. 10. Zheng, R., et al., Association between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database. Cardiovasc Diabetol, 2023. 22 (1): p. 307. Boshen, Y., et al., Triglyceride-glucose index is associated with the occurrence and prognosis of cardiac arrest: a multicenter retrospective observational study. Cardiovasc Diabetol, 2023. 22 (1): p. 190. Guo, J., et al., The triglycerides-glucose index and the triglycerides to high-density lipoprotein cholesterol ratio are both effective predictors of in-hospital death in non-diabetic patients with AMI. PeerJ, 2022. 10 : p. e14346. Cheng, L., et al., Association of dynamic change of triglyceride-glucose index during hospital stay with all-cause mortality in critically ill patients: a retrospective cohort study from MIMIC IV2.0. Cardiovasc Diabetol, 2023. 22 (1): p. 142. Alavi Tabatabaei, G., et al., Association of the triglyceride glucose index with all-cause and cardiovascular mortality in a general population of Iranian adults. Cardiovasc Diabetol, 2024. 23 (1): p. 66. Laakso, M. and J. Kuusisto, Insulin resistance and hyperglycaemia in cardiovascular disease development. Nat Rev Endocrinol, 2014. 10 (5): p. 293-302. Ormazabal, V., et al., Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol, 2018. 17 (1): p. 122. Shulman, G.I., Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med, 2014. 371 (23): p. 2237-8. Hedayatnia, M., et al., Dyslipidemia and cardiovascular disease risk among the MASHAD study population. Lipids Health Dis, 2020. 19 (1): p. 42. Li, M., et al., Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther, 2022. 7 (1): p. 216. Cho, K.H., et al., Low-density lipoprotein cholesterol level in patients with acute myocardial infarction having percutaneous coronary intervention (the cholesterol paradox). Am J Cardiol, 2010. 106 (8): p. 1061-8. Reddy, V.S., et al., Relationship between serum low-density lipoprotein cholesterol and in-hospital mortality following acute myocardial infarction (the lipid paradox). Am J Cardiol, 2015. 115 (5): p. 557-62. Navarese, E.P., et al., Association Between Baseline LDL-C Level and Total and Cardiovascular Mortality After LDL-C Lowering: A Systematic Review and Meta-analysis. Jama, 2018. 319 (15): p. 1566-1579. Wu, M., et al., Association of low-density lipoprotein-cholesterol with all-cause and cause-specific mortality. Diabetes Metab Syndr, 2023. 17 (6): p. 102784. Kovesdy, C.P., J.E. Anderson, and K. Kalantar-Zadeh, Inverse association between lipid levels and mortality in men with chronic kidney disease who are not yet on dialysis: effects of case mix and the malnutrition-inflammation-cachexia syndrome. J Am Soc Nephrol, 2007. 18 (1): p. 304-11. Tian, X., et al., Time course of the triglyceride glucose index accumulation with the risk of cardiovascular disease and all-cause mortality. Cardiovasc Diabetol, 2022. 21 (1): p. 183. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). Jama, 2001. 285 (19): p. 2486-97. Tables Table1. Baseline characteristics of the study population according to TyG index and LDL-C. LDL-C＜2.6 mmol/L LDL-C≥2.6 mmol/L T1 T2 T3 P value T1 T2 T3 P value Age, years 54.06±0.90 61.30±0.69 59.17±0.67 < 0.001 54.98±0.68 57.45±0.61 55.71±0.56 0.022 Female, n (%) 57.31 46.83 46.30 0.002 58.95 57.07 49.49 0.002 Race, n (%) < 0.001 < 0.001 Hispanic 10.84 11.79 13.95 12.01 13.50 20.66 non-Hispanic black 18.30 10.55 8.10 20.89 12.93 9.25 non-Hispanic white 62.80 70.36 69.62 60.09 66.50 63.10 other 8.06 7.30 8.33 7.01 7.07 7.00 Smoke, n (%) 0.148 0.053 never 50.99 46.47 42.99 55.16 51.14 46.29 former 30.84 34.61 38.48 25.89 28.65 31.51 current 18.18 18.92 18.53 18.95 20.20 22.20 Alcohol drinking, n (%) < 0.001 0.081 none 74.77 80.90 85.56 76.79 80.68 83.11 moderate 8.27 5.94 6.57 10.41 8.78 6.92 heavy 16.97 13.15 7.88 12.81 10.55 9.97 Insurance, n (%) 0.475 0.132 not covered any insurance 11.92 9.54 9.81 15.52 12.54 17.33 other insurance 27.32 30.39 27.56 28.83 28.54 27.05 private insurance 60.77 60.07 62.63 55.66 58.93 55.62 Poverty index 0.396 0.234 <1.3.0 25.42 22.47 21.28 27.05 27.81 28.91 1.30-2.99 31.44 35.99 33.01 31.09 36.10 32.46 ≥3.00 43.13 41.54 45.71 41.86 36.09 38.63 Education, n (%) 0.006 0.01 below high school 35.97 38.35 33.03 35.23 36.93 40.16 high school 10.04 15.08 16.43 12.84 17.75 17.35 college or above 53.99 46.58 50.54 51.93 45.31 42.49 Matrimony, n (%) < 0.001 0.006 never married 14.83 6.92 8.50 14.01 8.93 10.56 divorced/separated/widowed 23.57 25.75 27.85 29.46 27.49 25.52 married/living with partner 61.59 67.33 63.65 56.53 63.58 63.92 Physical activity < 0.001 0.026 moderate 31.52 36.84 35.55 30.71 29.95 32.41 sedentary 35.45 39.97 45.51 37.44 43.77 43.77 vigorous 33.03 23.20 18.94 31.85 26.28 23.82 SBP, mmHg 123.20±0.91 128.18±0.91 130.71±0.79 < 0.001 128.89±0.76 130.72±0.88 131.55±0.67 0.032 DBP, mmHg 66.41±0.54 67.94±0.62 71.36±0.59 < 0.001 70.78±0.56 72.40±0.56 73.21±0.54 0.007 BMI, kg.m 2 27.47±0.37 30.70±0.33 33.16±0.32 < 0.001 28.94±0.31 31.89±0.36 32.44±0.30 < 0.001 Laboratory indexes HbA1c, % 5.66±0.04 6.11±0.04 7.04±0.09 < 0.001 5.65±0.03 5.99±0.03 7.11±0.07 < 0.001 NLR 2.54±0.07 2.46±0.05 2.45±0.05 0.567 2.36±0.05 2.32±0.04 2.28±0.05 0.523 WBC, 10 9 /L 6.60±0.11 7.22±0.10 7.78±0.09 < 0.001 6.71±0.08 7.30±0.09 7.56±0.08 < 0.001 total cholesterol, mmol/L 3.92±0.04 3.97±0.03 4.20±0.02 < 0.001 5.33±0.03 5.53±0.04 5.92±0.03 < 0.001 HDL-C, mmol/L 1.62±0.03 1.33±0.01 1.11±0.01 < 0.001 1.54±0.02 1.33±0.01 1.16±0.01 < 0.001 BUN, mmol/L 5.41±0.11 5.71±0.13 5.72±0.13 0.081 5.20±0.07 5.41±0.08 5.28±0.09 0.164 uric acid, umol/L 316.97±3.56 347.40±4.32 356.92±4.48 < 0.001 332.44±2.89 354.90±3.69 354.57±3.72 < 0.001 uACR, mg/g 78.04±15.18 65.46±7.95 111.76±21.58 0.148 69.02±7.19 73.21±6.73 135.69±22.61 0.019 eGFR, mL/min/1.73m 2 88.09±1.26 80.23±1.16 83.47±1.12 < 0.001 86.88±0.92 84.81±0.98 87.77±0.92 0.055 Medical history CVD, n (%) 40.75 40.44 33.40 0.050 27.33 26.73 26.46 0.932 anemia, n (%) 19.04 11.65 8.31 < 0.001 8.90 6.99 5.33 0.018 HT, n (%) 47.65 62.65 69.90 < 0.001 47.94 54.58 52.39 0.057 CKD, n (%) 58.81 46.48 42.97 < 0.001 60.90 51.32 43.67 < 0.001 DM, n (%) 31.93 54.61 81.83 < 0.001 28.73 48.88 70.91 < 0.001 Drug history glucose-lowering drug, n (%) 20.70 35.12 57.24 < 0.001 11.16 19.50 35.99 < 0.001 antihypertensive drug, n (%) 50.23 73.00 74.54 < 0.001 42.90 50.36 51.83 0.004 lipid-lowering drug, n (%) 43.64 63.37 64.29 < 0.001 17.90 24.60 27.95 < 0.001 Data are means ± SD, median (interquartile range), or n (%) Abbreviation: TyG, triglyceride-glucose; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HbA1c, glucated hemoglobin; NLR, neutrophil/lymphocyte ratio; WBC, white blood cell; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen; uACR, urea albumin creatinine ratio; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease; HT, hypertension; CKD, chronic kidney disease; DM, diabetes mellitus; T, tristile. Table 2. Risk of all-cause and cardiovascular mortality upon co-exposure stratified by the TyG index and LDL-C. All-cause mortality Cardiovascular mortality HR (95%CI) P value HR (95%CI) P value LDL-C ≥2.6 & TyG T1 reference reference LDL-C ≥2.6 & TyG T2 1.06(0.83,1.37) 0.632 1.05(0.69,1.60) 0.813 LDL-C ≥2.6 & TyG T3 1.47(1.15,1.88) 0.002 1.65(1.09,2.49) 0.018 LDL-C <2.6 & TyG T1 1.59(1.22,2.08) <0.001 1.64(1.04,2.58) 0.032 LDL-C <2.6 & TyG T2 1.24(0.93,1.66) 0.147 1.35(0.88,2.05) 0.168 LDL-C <2.6 & TyG T3 1.33(1.00,1.75) 0.046 1.29(0.83,1.99) 0.257 Model adjusted for age, gender, race, alcohol drinking, smoking, physical activity, poverty index, education, matrimony, insurance, body mass index, hypertension, diabetes, chronic kidney disease, cardiovascular disease, anemia, neutrophil/lymphocyte ratio, high-density lipoprotein cholesterol, uric acid, blood urea nitrogen, glucose-lowering drug, antihypertensive drug, and lipid-lowering drug. Abbreviation: TyG, triglyceride-glucose; LDL-C, low-density lipoprotein cholesterol; T, tristile; HR, hazard ratio. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx Cite Share Download PDF Status: Published Journal Publication published 18 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Nov, 2024 Reviews received at journal 03 Nov, 2024 Reviewers agreed at journal 03 Nov, 2024 Reviews received at journal 02 Nov, 2024 Reviews received at journal 19 Oct, 2024 Reviewers agreed at journal 19 Oct, 2024 Reviewers agreed at journal 16 Oct, 2024 Reviewers agreed at journal 13 Oct, 2024 Reviewers agreed at journal 11 Oct, 2024 Reviewers invited by journal 23 Aug, 2024 Editor assigned by journal 23 Aug, 2024 Editor invited by journal 12 Aug, 2024 Submission checks completed at journal 10 Aug, 2024 First submitted to journal 10 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4890377","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":346234579,"identity":"f7631bbc-07b3-4367-897d-9b5f6dcbd785","order_by":0,"name":"Wenguang Lai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNobDBx9I8LDVz5d/fIA4LXyMx5INLGT4GDc2pCUQp0WO+YyZRIWNHGPDgRwDIh3GdsBM4kaOGTNjw5mPN94w2MnpNhDSwnMg2XLGmTQ2dsbezZZzGJKNzQ4Q0iJx4OBtyZ5jPIzNvNukeRgOJG4jqEX+YYP033//JRiO8TwjUgvDYSYJYCAbMJzhYSNWyzFmA6CWBMMZbMaWcwyI8It8w/mPoKhMkJdgfnjjTYWdHEEtKECCh8ioQdZCqo5RMApGwSgYEQAAL40/qJoIDbcAAAAASUVORK5CYII=","orcid":"","institution":"Heyuan People's Hospital, Guangdong Provincial People's Hospital, Heyuan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wenguang","middleName":"","lastName":"Lai","suffix":""},{"id":346234580,"identity":"bf3dcf37-f7be-4646-8b94-eee3277f89ac","order_by":1,"name":"Yucui Lin","email":"","orcid":"","institution":"Heyuan People's Hospital, Guangdong Provincial People's Hospital, Heyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yucui","middleName":"","lastName":"Lin","suffix":""},{"id":346234581,"identity":"189a4c4f-5d69-4e8c-af51-42fe4cb73713","order_by":2,"name":"Zhiyong Gao","email":"","orcid":"","institution":"Heyuan People's Hospital, Guangdong Provincial People's Hospital, Heyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Gao","suffix":""},{"id":346234582,"identity":"9e24036d-d5fc-4edc-afd9-c72f2eb10242","order_by":3,"name":"Zhidong Huang","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Zhidong","middleName":"","lastName":"Huang","suffix":""},{"id":346234583,"identity":"4b231a5b-e03e-4d53-8813-d1f32df2b65f","order_by":4,"name":"Tingting Zhang","email":"","orcid":"","institution":"Heyuan People's Hospital, Guangdong Provincial People's Hospital, Heyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-08-10 06:57:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4890377/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4890377/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-87416-7","type":"published","date":"2025-02-18T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66083956,"identity":"83166ce2-e6ce-42c1-94ff-16f45ca02de2","added_by":"auto","created_at":"2024-10-07 14:14:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviation: eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4890377/v1/61c40858ab57f4fc0f71cd82.png"},{"id":66081842,"identity":"cadfe7e1-3245-4d86-a55a-0fe27c132f10","added_by":"auto","created_at":"2024-10-07 14:06:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe incidence of all-cause and cardiovascular death is stratified by the TyG index and LDL-C\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviation: TyG, triglyceride-glucose; LDL-C, low-density lipoprotein cholesterol\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4890377/v1/f8659ba1bb3a3f0878e3ae0b.png"},{"id":66081843,"identity":"7da86bcd-a735-48d2-8f7e-b37aa9c17af4","added_by":"auto","created_at":"2024-10-07 14:06:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe joint association between TyG index and LDL-C for all-cause and cardiovascular mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel adjusted for age, gender, race, alcohol drinking, smoking, physical activity, poverty index, education, matrimony, insurance, body mass index, hypertension, diabetes, chronic kidney disease, cardiovascular disease, anemia, neutrophil/lymphocyte ratio, high-density lipoprotein cholesterol, uric acid, blood urea nitrogen, glucose-lowering drug, antihypertensive drug, and lipid-lowering drug.\u003c/p\u003e\n\u003cp\u003eAbbreviation: TyG, triglyceride-glucose; LDL-C, low-density lipoprotein cholesterol; T, tristile; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4890377/v1/58180d1c2fd30ad8273c1706.png"},{"id":66081846,"identity":"4173fa4b-0171-4063-8e01-30abcc32814a","added_by":"auto","created_at":"2024-10-07 14:06:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":128913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe subgroups analyses of the joint association between TyG index and LDL-C for all-cause and cardiovascular mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the limited sample size, the subgroups analyses model adjusted for age, gender, race, alcohol drinking, smoking, physical activity, poverty index, education, matrimony, insurance, and body mass index.\u003c/p\u003e\n\u003cp\u003eAbbreviation: TyG, triglyceride-glucose; LDL-C, low-density lipoprotein cholesterol; T, tristile; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4890377/v1/1b08674bb7f5c822c1dc06d1.png"},{"id":66081845,"identity":"f7e649fe-8613-4cba-9967-85656e5b15a0","added_by":"auto","created_at":"2024-10-07 14:06:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":140632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios for the all-cause, and cardiovascular death based on restricted cubic spine function for the TyG index and LDL-C\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll restricted cubic spline analyses were adjusted for age, gender, race, alcohol drinking, smoking, physical activity, poverty index, education, matrimony, insurance, body mass index, hypertension, diabetes, chronic kidney disease, cardiovascular disease, anemia, neutrophil/lymphocyte ratio, high-density lipoprotein cholesterol, uric acid, blood urea nitrogen, glucose-lowering drug, antihypertensive drug, and lipid-lowering drug.\u003c/p\u003e\n\u003cp\u003eAbbreviation: TyG, triglyceride-glucose; LDL-C, low-density lipoprotein cholesterol; T, tristile; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4890377/v1/3af5a36d2163c6acb5e94df2.png"},{"id":77058224,"identity":"f4d46bad-0d8d-468f-a9c5-11d25b1e658e","added_by":"auto","created_at":"2025-02-24 16:54:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1563400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4890377/v1/1aef8812-c114-452c-bcbf-2cd6b3167a48.pdf"},{"id":66081847,"identity":"2f12b773-5a90-4ccf-80a7-dd6c71212389","added_by":"auto","created_at":"2024-10-07 14:06:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1032804,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4890377/v1/5dd668bffd61d75ec8ebd776.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint association of TyG index and LDL-C with all-cause and cardiovascular mortality among patients with cardio-renal-metabolic disease","fulltext":[{"header":"Background","content":"\u003cp\u003eCardiac, renal, and metabolic (CRM) conditions remain a leading cause of morbidity and mortality in the United States, which is estimated to account for about a third of contemporary deaths[1]. Cardiovascular, kidney, and metabolic functions are closely linked[2], and there are common pathophysiological and risk factors with the onset of the diseases[3]. The impairment in one system may promote and amplify dysfunction in the others, leading to subsequent morbidity and mortality[4-6]. Therefore, the joint management of risk factors for the risk assessment and improvement of the prognosis among CRM patients is crucial.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Insulin resistance (IR), characterized by reduced sensitivity or responsiveness to insulin metabolism, has been widely recognized as an independent risk factor for death among CRM patients\u0026nbsp;[3]. The triglyceride–glucose index (TyG) has been considered a dependable and surrogate biomarker for reflecting IR[7]. Current research has clearly established a strong association between the TyG index and mortality within high-risk population, such as those with acute myocardial infarction (AMI)[8], acute heart failure (AHF)[9], and patients in intensive care units (ICU)[10]. The prognostic potential of the TyG index is also being increasingly explored among population at moderate to low risk, including those with chronic kidney disease(CKD)[11], diabetes (DM)[12], and the general population[13]. However, the relationships in these population remain unclear. Further stratification and detailed analysis may be necessary to clarify these associations.\u003c/p\u003e\n\u003cp\u003eLow-density lipoprotein cholesterol (LDL-C) can permeate arterial walls, accumulate within the intima, and promote the generation of atherosclerotic plaque, which can significantly constitute a major risk factor for long-term mortality of CRM patients[14, 15]. Previous studies have shown that joint assessment and management of the TyG index with other risk factors is essential for risk stratification and improvement of prognosis in the community population. Cui et al found the joint assessment of TyG index and inflammation contributes to residual risk stratification and primary prevention of cardiovascular disease (CVD)[16]. A growing body of research found that TyG index demonstrated a strong association with dyslipidemia, which displayed superior risk stratification for the long-term prognosis[17, 18]. Therefore, there is potential for a significant interaction between the TyG index and LDL-C in relation to long-term mortality, however, the precise nature of this interaction is currently not well understood in the CRM population.\u003c/p\u003e\n\u003cp\u003eTherefore, the objection of this research was to investigate the joint effect and risk reclassification of TyG index and LDL-C with all-cause and cardiovascular mortality, based on a large-scale prospective cohort, which may be beneficial to the clinical management of CRM patients in clinical practice.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed data from the National Health and Nutrition Examination Survey (NHANES) database, a program administered by the Centers for Disease Control and Prevention and the National Centers for Health Statistics in the US. CRM disease is a constellation of conditions that includes CVD, CKD, as well as DM[19], and patients with CRM disease were included in this research. The Research Ethics Review Committee of the National Center for Health Statistics has approved the NHANES study, and all participants provided written informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cohort included a total of 21,604 patients with CRM during 1999\u0026ndash;2018. Patients meeting the following criteria were excluded: (1) participants with age \u0026lt;18; (2) receiving dialysis treatment or combining with renal failure (estimated glomerular filtration rate (eGFR)＜15\u0026nbsp;ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e) in the past year; (3) combined with malignant tumor; (4) participants experiencing pregnancy; (5) missing information on TyG index (fasting blood glucose [FBG] or triglyceride\u0026nbsp;[TG]), or LDL-C; (6) missing information on mortality or survival time. Finally, a total of 6,010 eligible patients with CRM were enrolled in the study (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes and exposure definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main outcomes of this research were all-cause and cardiovascular mortality. Information about mortality status and the cause of death was gained from the NHANES Linked Mortality File, which was created by the National Center for Health Statistics (NCHS) by linking the NHANES data to the National Death Index (NDI). NHANES used an autoanalyzer to enzymatically measure plasma TG, FPG, and LDL-C levels from fasting blood samples. The TyG index was calculated by Ln [TG (mg/dL) \u0026times; FPG (mg/dl)/2].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline variables, including demographic data (age, gender, educational levels, race, insurance, poverty index, and matrimony), lifestyle variables (smoking status, alcohol drinking, and physical activity), medical history(CVD, anemia, hypertension[HT], CKD, DM), drug history(glucose-lowering drugs, antihypertensive drugs, as well as lipid-lowering drugs), anthropometric measurements(height, weight, body mass index[BMI], systolic blood pressure[SBP], and diastolic blood pressure[DBP]), and laboratory variables (hemoglobin A1c [HbA1c], high-density lipoprotein cholesterol[HDL-C], neutrophil/lymphocyte ratio[NLR], total cholesterol, white blood cell[WBC], blood urea nitrogen[BUN], serum creatinine, serum uric acid, eGFR, and urinary albumin/creatinine ratio[uACR]) were selected. Demographics, lifestyle variables, medical history, and drug history were collected through self-reported questionnaires. Anthropometric indicators and biochemical parameters were obtained through medical examinations and laboratory tests, respectively. Further details can be found at https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003eVariables including smoking (current smoker, former smoker, and never smoked), alcohol drinking (none, moderate, and\u0026nbsp;heavy), education (below high school,\u0026nbsp;high school, and college or above), matrimony (never married,\u0026nbsp;divorced/separated/widowed, and married/living with partner) and physical activity (PA) (moderate, sedentary, and vigorous) were further defined. CVD is composed of heart attack, congestive heart failure, angina, CAD, and stroke[20]. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation[21], and CKD was defined as eGFR\u0026thinsp;\u0026le;\u0026thinsp;60 ml/min/1.73 m2 or uACR \u0026ge;30 mg/g. Diabetes was defined as fasting glucose \u0026ge; 7.0 mmol/L or HbA1c(%)\u0026ge;6.5 or self-reported diagnosis history of diabetes or use of any hypoglycemic medication[22]. Anemia is defined as men with a hemoglobin level less than 130 g/L and women with a hemoglobin level less than 120 g/L according to World Health Organization standards[23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using a complex, multistage probability sampling design. The study includes data from eight distinct survey cycles over a period of 18 years, starting with the initial phase from 1999 to 2002 and continuing with biennial cycles from 2003 to 2018. Fasting sample weights were applied in accordance with the NHANES Analytical Guidelines.\u003c/p\u003e\n\u003cp\u003eBaseline characteristics between groups were compared using weighted analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Restricted cubic splines (RCS) analysis was utilized to assess the dose-response relationships of the TyG index with all-cause and cardiovascular mortality in LDL-C \u0026lt; 2.6 mmol/L and \u0026ge; 2.6 mmol/L, respectively. Kaplan-Meier method was applied to time-to-event endpoints, with a stratified log-rank test used to compare differences among groups defined by TyG and LDL-C levels. Cox regression analysis was used to evaluate the individual and joint effects of the TyG index and LDL-C on cardiovascular and all-cause mortality. To evaluate whether there is a linear trend in mortality risk across increasing exposure groups, we transformed the categorical exposure groups into a numerical scale. This transformation served as the primary predictor in our model. The estimated coefficient for this variable quantifies the trend in mortality risk corresponding to escalating levels of exposure. To explore the potential interaction between TyG index and LDL-C in relation to mortality, an interaction term (TyG * LDL-C) was introduced into the model. The significance of the interaction was assessed using a likelihood ratio test, comparing the fit of the model with and without the interaction term. Subgroup analyses were conducted, stratified by the presence of CVD, CKD, and DM. Missing covariates were handled using multiple imputations, and a sensitivity analysis was performed. A list of missing covariates is provided in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003eThe analyses were performed using R software (Version 4.2.3), and statistical significance was determined using two-tailed tests with a threshold of P\u0026lt;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included 6,010 CRM patients in the final analysis. The mean age of the patients was 56.96 ± 0.31 years, with females accounting for approximately 52.97%. Table 1 presents the baseline characteristics according to LDL-C and TyG tertiles. Patients with elevated TyG index tend to be male and often have comorbidities like anemia, CKD, and DM. They show higher levels of BMI, SBP, DBP, WBC, HbA1c, and uric acid. Moreover, these patients are more likely to receive glucose-lowering drugs, antihypertensive drugs, and lipid-lowering drugs, irrespective of their LDL-C levels being \u0026lt; 2.6 mmol/L or\u0026nbsp;≥2.6 mmol/L. While patients with higher TyG index were more likely to be drinking; and combined HT in the LDL-C＜2.6 mmol/L group; and have a higher level of uACR in the LDL-C ≥ 2.6 mmol/L group (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith a median follow-up period was 7.6 years ([interquartile range: 4.1–11.9]), there were 1370 (22.8%) and 504 (8.4%) patients who died from all-cause and cardiovascular death, respectively.\u0026nbsp;Participants with elevated TyG index have higher all-cause mortality (P for trend=0.001) and cardiovascular mortality (P for trend=0.008) among patients with LDL-C ≥ 2.6 mmol/L. However, an increase in long-term all-cause mortality (P for trend=0.351) and cardiovascular mortality (P for trend=0.540) with higher TyG index were not statistically significant for LDL-C \u0026lt; 2.6 mmol/L (Figure 2). Further analysis revealed that among participants with LDL-C \u0026lt; 2.6 mmol/L, an increase in the TyG index was associated with an even decreasing trend in short-term mortality, including both all-cause mortality (P for trend = 0.010) and cardiovascular mortality (P for trend = 0.020) (Supplementary Figure 1). Survival curve analysis consistently demonstrated that the association between the TyG index and the risk of all-cause (P = 0.003) and cardiovascular (P = 0.010) mortality varied according to LDL-C stratification (Supplementary Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe association of TyG index, and LDL-C with all-cause and cardiovascular mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter adjusting for confounders, neither individual TyG index nor LDL-C were independently associated with all-cause or cardiovascular mortality (Supplementary Table 2). However, the combined analysis revealed significant associations between categories of LDL-C and TyG index and long-term mortality. Among patients with LDL-C≥2.6 mmol/L, increasing TyG index was linked to higher risks of all-cause and cardiovascular mortality. Specifically, compared to the first tertile (T1), the hazard ratios (HRs) for the second (T2) and third tertiles (T3) were 1.01 (95%CI: 0.79–1.28) and 1.38 (95%CI: 1.07–1.79) for all-cause mortality, and 1.09 (95%CI: 0.72–1.65) and 1.80 (95%CI: 1.18–2.75) for cardiovascular mortality, respectively, with a P for trend of 0.009 for all-cause mortality and 0.005 for cardiovascular mortality. However, there was no statistically significant association between TyG index and all-cause or cardiovascular mortality in patients with LDL-C \u0026lt; 2.6 mmol/L. Specifically, the HRs for all-cause mortality for T2 and T3 compared to T1 were 0.81 (95%CI: 0.59–1.09) and 0.87 (95%CI: 0.62–1.22), respectively, with a P for trend of 0.468. For cardiovascular mortality, the HRs were 0.80 (95%CI: 0.48–1.32) and 0.72 (95%CI: 0.45–1.15), respectively, with a P for trend of 0.173. Interactive analyses further revealed significant interactions between TyG index and LDL-C in predicting risks for all-cause (P for interaction = 0.011) and cardiovascular mortality (P for interaction = 0.050) (Figure 3). Further analysis also found that compared with patients with LDL-C ≥2.6 mmol/L and TyG in T1, those with LDL-C ≥ 2.6 mmol/L and TyG in T3 showed increased risks of all-cause mortality (HR: 1.47, 95%CI: 1.15–1.88, P=0.002) and cardiovascular mortality (HR: 1.65, 95%CI: 1.09–2.49, P=0.018). Similarly, patients with LDL-C \u0026lt; 2.6 mmol/L and TyG in T1 also exhibited elevated risks for all-cause mortality (HR: 1.59, 95%CI: 1.22–2.08, P\u0026lt;0.001) and cardiovascular mortality (HR: 1.64, 95% CI: 1.04–2.58, P=0.032)(Table 2). Further subgroup analysis also showed a similar trend (Figure 4). The results also remained consistent in analyses conducted on datasets after the imputation of missing data (Supplementary Table 3 and Supplementary Figure 3). Restricted cubic spline analyses also demonstrated a nearly linear relationship between the continuous scale TyG and both all-cause mortality (nonlinear P=0.182) and cardiovascular mortality (nonlinear P=0.244) in patients with LDL-C≥2.6 mmol/L, Conversely, no significant relationship was observed between TyG index and either all-cause mortality and or cardiovascular mortality among patients with LDL-C＜2.6 mmol/L(Figure 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first prospective study that evaluated the joint association of TyG index and LDL-C levels with the subsequent risk of all-cause and cardiovascular mortality among CRM patients. Our research found the associations of TyG index with all-cause and cardiovascular mortality were modified by LDL-C levels. Elevated TyG index can significantly increase the risk of all-cause and cardiovascular mortality only among CRM patients with LDL-C ≥ 2.6 mmol/L, but not among patients with LDL-C \u0026lt; 2.6 mmol/L. Further analysis also observed a significant interaction.\u0026nbsp;Therefore,\u0026nbsp;joint assessment of TyG index and LDL-C should be emphasized for risk stratification and improvement of long-term prognosis among CRM patients\u0026nbsp;in the community.\u003c/p\u003e\n\u003cp\u003ePrevious research has found that individual TyG index is significantly associated with the risk of mortality in emergency and critically ill population. A higher baseline TyG index significantly increases the risk of mortality among critically ill patients[24-26]. Zheng et al. also found that baseline TyG is related to the prognosis of cardiac arrest[27]. Further research also found the dynamic changes in TyG during hospitalization were related to one-year all-cause death among critically ill patients with CAD[28]. Therefore, the TyG index has important prognostic significance among the critically ill population in clinical practice. However, in the general or community population, the effect of individual TyG index on long-term prognosis was weak, even without statistical difference. Chen\u0026nbsp;et al. demonstrated that after full adjustment, a significant relationship\u0026nbsp;between the TyG stratification and all-cause and cardiovascular death in the general population was not observed\u0026nbsp;[29]. Ghazaal\u0026nbsp;et al. consistently found that after adjusting for DM, the effects of TyG index on all-cause and cardiovascular death disappeared, and TyG index was not associated with the risk of non-cardiovascular death in the general population[30]. Our study also found that individual TyG index was not associated with all-cause and cardiovascular death among CRM subjects in the community cohort.\u0026nbsp;Therefore, further studies are needed to explore risk reclassification in the general community population. Previous research showed that TyG index was positively related to lipid-related markers by Pearson correlation analysis, including LDL-C (r=0.238, P\u0026lt;0.001) in AMI patients without DM[28].\u0026nbsp;However, there has been no research to explore the joint association of TyG index and LDL-C with the risk of long-term mortality among CRM patients in the community. Our study found that there is a significant joint association of TyG index and LDL-C with all-cause and cardiovascular mortality among CRM patients, which is no difference between all-cause and cardiovascular mortality on an elevated TyG index in patients with LDL-C \u0026lt; 2.6 mmol/L, while higher TyG index was related to significantly increased risk of all-cause and cardiovascular mortality among patients with LDL-C ≥ 2.6 mmol/L.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe underlying mechanisms of the joint effect of TyG index and LDL-C remain unknown. The TyG index is significantly associated with long-term prognosis and has been recognized as a reliable and newfound biomarker for gauging IR[7]. Pathophysiological research has indicated IR can induce low-grade inflammation, dyslipidemia, and chronic hyperglycemia, which may serve as important mechanisms for the increased risk of death[31-33]. Moreover, IR induces dyslipidemia, mainly manifested as an increase in serum total cholesterol, LDL-C, or triglycerides, together increasing the mortality of all-cause and cardiovascular death[34]. Furthermore, the accumulation of triglyceride and LDL-C in hepatocytes will further promote IR through inflammation, oxidative stress, and lipotoxicity[35]. Therefore, the joint effect of IR with\u0026nbsp;dyslipidemia may significantly increase the risk of all-cause and cardiovascular mortality.\u003c/p\u003e\n\u003cp\u003eInterestingly, findings from our studies suggested an association of low LDL-C levels with a higher risk of all-cause and cardiovascular death among CRM patients, and subgroup analysis also showed a consistent trend. A growing body of research has also shown a similar relationship. Previous research indicated a paradoxical relationship between low LDL-C levels and a higher risk of death among patients with acute coronary syndrome[36, 37]. In addition, a recent meta-analysis demonstrated that the benefits of lowering LDL-C did not exist when the LDL-C \u0026lt; 2.6 mmol/L[38]. Consistently, Wu et al, also found low LDL-C levels increased the risk of all-cause and cause-specific death[39]. A plausible cause for the lack of a significant correlation between LDL-C and long-term prognosis is that CRM subjects with low LDL-C levels\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ewere in poor condition and had more complications. Our research also showed that patients with low LDL-C levels had a higher prevalence of CVD-related diseases, as well as other complications related to poor prognosis, including anemia (12.94% vs. 7.04%), HT (59.99% vs. 51.48%), and poor renal function. Similarly, Kovesdy et al. also found that the paradoxical association of low LDL-C levels with a higher risk of mortality attenuated as the adjusted variables increased. After further correcting for confounders, low LDL-C levels were no longer independently associated with an increased risk of death[40]. Therefore, the higher all-cause and cardiovascular death among participants with low LDL-C levels may be caused by underlying disease to some extent.\u003c/p\u003e\n\u003cp\u003eUnderstanding the joint associations of TyG index and LDL-C levels with all-cause and cardiovascular death and their predictive values in long-term prognosis could help identify patients at high risk of poor prognosis, and enhance the understanding of pathophysiological mechanisms. Hence, the\u0026nbsp;joint evaluation of TyG and LDL-C in routine clinical practice is essential to improve prognosis among patients with CRM.\u0026nbsp;Besides, our results highlighted the importance of regular detection of TyG index and LDL-C may reduce the risk of all-cause and cardiovascular death with the most appropriate interventions as soon as possible. In addition, excessively low LDL-C levels in the assessment of the risk of long-term death may serve as a newfound indicator in clinical practice, and if necessary, screen for related comorbidities that may affect death. Ultimately, further high-quality prospective research for the joint effect between TyG index and LDL-C in CRM patients should be conducted to evaluate the appropriate management strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eseveral limitations need to be addressed. Firstly, despite adjusting for potential confounders, there are still residual confounding effects of indefinite factors that may contribute to the increased risk of death. Second, this study only explored the baseline TyG index, and did not evaluate how the temporal variation of this index affects its association with death. Tian et al. also have shown that different periods of cumulative TyG lead to different risks of mortality[41]. Thirdly, due to sample size limitations, we did not further evaluate the relationship between TyG index and mortality, based on the cutoff of LDL-C\u0026lt;1.8 mmol/L. However, The National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) guidelines recommends 2.6 mmol/L as an appropriate cutoff in a community cohort[42]. Finally, the NHANES study was conducted only in the United States, and it is unclear whether our findings are applicable to other regions. Therefore, further high-quality studies are needed to verify our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research found the relationship between TyG index and all-cause and cardiovascular mortality was modified by LDL-C levels. Elevated TyG index can significantly increase the risk of all-cause and cardiovascular mortality only among CRM patients with LDL-C \u0026ge; 2.6 mmol/L, but not among patients with LDL-C \u0026lt; 2.6 mmol/L. Joint assessment of TyG index and LDL-C levels should be emphasized for risk stratification and improvement of long-term prognosis among CRM patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our heartfelt gratitude to all participants and investigators involved in the NHANES study. Your invaluable contributions have been instrumental to our research endeavors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent has been obtained from every participant and therefore there was no need for any ethical consent in this study. The NCHS ethics review board has approved the NHANES protocol. All procedures were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no competing interests for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded and supported by Guangdong Medical Research Foundation (A2024142).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026rsquo; responsibilities were as follows\u0026mdash;(I) Research idea and study design: Wenguang Lai, Yucui Lin. (Ⅱ) Statistical analysis: Wenguang Lai, Zhidong Huang; (Ⅲ) Supervision and mentorship: Tingting Zhang; (Ⅳ) revised the manuscript: Zhiyong Gao.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available as publicly accessible datasets through NHANES. It is open and publicly accessible through the following link: https://wwwn.cdc.gov/nchs/nhanes/.\u003c/p\u003e"},{"header":"Reference","content":"\u003col\u003e\n\u003cli\u003eAhmad, F.B. and R.N. Anderson, \u003cem\u003eThe Leading Causes of Death in the US for 2020.\u003c/em\u003e Jama, 2021. \u003cstrong\u003e325\u003c/strong\u003e(18): p. 1829-1830.\u003c/li\u003e\n\u003cli\u003eSarafidis, P., et al., \u003cem\u003eSGLT-2 inhibitors and GLP-1 receptor agonists for nephroprotection and cardioprotection in patients with diabetes mellitus and chronic kidney disease. A consensus statement by the EURECA-m and the DIABESITY working groups of the ERA-EDTA.\u003c/em\u003e Nephrol Dial Transplant, 2019. \u003cstrong\u003e34\u003c/strong\u003e(2): p. 208-230.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eCardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment.\u003c/em\u003e Lancet Diabetes Endocrinol, 2014. \u003cstrong\u003e2\u003c/strong\u003e(8): p. 634-47.\u003c/li\u003e\n\u003cli\u003eCherney, D.Z.I., et al., \u003cem\u003eImpact of Cardio-Renal-Metabolic Comorbidities on Cardiovascular Outcomes and Mortality in Type 2 Diabetes Mellitus.\u003c/em\u003e Am J Nephrol, 2020. \u003cstrong\u003e51\u003c/strong\u003e(1): p. 74-82.\u003c/li\u003e\n\u003cli\u003eForman, D.E., et al., \u003cem\u003eMultimorbidity in Older Adults With Cardiovascular Disease.\u003c/em\u003e J Am Coll Cardiol, 2018. \u003cstrong\u003e71\u003c/strong\u003e(19): p. 2149-2161.\u003c/li\u003e\n\u003cli\u003eThomas, G., et al., \u003cem\u003eMetabolic syndrome and kidney disease: a systematic review and meta-analysis.\u003c/em\u003e Clin J Am Soc Nephrol, 2011. \u003cstrong\u003e6\u003c/strong\u003e(10): p. 2364-73.\u003c/li\u003e\n\u003cli\u003eTahapary, D.L., et al., \u003cem\u003eChallenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index.\u003c/em\u003e Diabetes Metab Syndr, 2022. \u003cstrong\u003e16\u003c/strong\u003e(8): p. 102581.\u003c/li\u003e\n\u003cli\u003eDrwiła, D., et al., \u003cem\u003ePrognostic value of the triglyceride-glucose index among non-diabetic patients with acute myocardial infarction at one-year follow-up.\u003c/em\u003e Kardiol Pol, 2021. \u003cstrong\u003e79\u003c/strong\u003e(10): p. 1116-1123.\u003c/li\u003e\n\u003cli\u003eCheng, H., et al., \u003cem\u003eThe triglyceride glucose index predicts short-term mortality in non-diabetic patients with acute heart failure.\u003c/em\u003e Adv Clin Exp Med, 2024. \u003cstrong\u003e33\u003c/strong\u003e(2): p. 103-110.\u003c/li\u003e\n\u003cli\u003eLiao, Y., et al., \u003cem\u003eTriglyceride-glucose index linked to all-cause mortality in critically ill patients: a cohort of 3026 patients.\u003c/em\u003e Cardiovasc Diabetol, 2022. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 128.\u003c/li\u003e\n\u003cli\u003eQin, Y., et al., \u003cem\u003eTriglyceride-glucose index and mortality risk in individuals with or without chronic kidney disease: Insights from a national survey of United States adults, 1999-2018.\u003c/em\u003e Nutr Metab Cardiovasc Dis, 2024. \u003cstrong\u003e34\u003c/strong\u003e(8): p. 1994-2001.\u003c/li\u003e\n\u003cli\u003eLiu, C., et al., \u003cem\u003eAssociation between the triglyceride-glucose index and all-cause and CVD mortality in the young population with diabetes.\u003c/em\u003e Cardiovasc Diabetol, 2024. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 171.\u003c/li\u003e\n\u003cli\u003eChen, J., et al., \u003cem\u003eAssociation of triglyceride glucose index with all-cause and cardiovascular mortality in the general population.\u003c/em\u003e Cardiovasc Diabetol, 2023. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 320.\u003c/li\u003e\n\u003cli\u003eValensi, P., et al., \u003cem\u003eThe impact of diabetes on heart failure development: The cardio-renal-metabolic connection.\u003c/em\u003e Diabetes Res Clin Pract, 2021. \u003cstrong\u003e175\u003c/strong\u003e: p. 108831.\u003c/li\u003e\n\u003cli\u003eWhaley-Connell, A. and J.R. Sowers, \u003cem\u003eBasic science: Pathophysiology: the cardiorenal metabolic syndrome.\u003c/em\u003e J Am Soc Hypertens, 2014. \u003cstrong\u003e8\u003c/strong\u003e(8): p. 604-6.\u003c/li\u003e\n\u003cli\u003eCui, C., et al., \u003cem\u003eJoint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study.\u003c/em\u003e Cardiovasc Diabetol, 2024. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 156.\u003c/li\u003e\n\u003cli\u003eWen, J., et al., \u003cem\u003eAssociation of triglyceride-glucose index with atherosclerotic cardiovascular disease and mortality among familial hypercholesterolemia patients.\u003c/em\u003e Diabetol Metab Syndr, 2023. \u003cstrong\u003e15\u003c/strong\u003e(1): p. 39.\u003c/li\u003e\n\u003cli\u003eZhao, K., et al., \u003cem\u003eTriglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio predict the prognosis in patients with type B aortic dissection receiving thoracic endovascular aortic repair.\u003c/em\u003e J Thorac Dis, 2024. \u003cstrong\u003e16\u003c/strong\u003e(3): p. 1971-1983.\u003c/li\u003e\n\u003cli\u003eOstrominski, J.W., et al., \u003cem\u003ePrevalence and Overlap of Cardiac, Renal, and Metabolic Conditions in US Adults, 1999-2020.\u003c/em\u003e JAMA Cardiol, 2023. \u003cstrong\u003e8\u003c/strong\u003e(11): p. 1050-1060.\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., \u003cem\u003eExposure to acrylamide and the risk of cardiovascular diseases in the National Health and Nutrition Examination Survey 2003-2006.\u003c/em\u003e Environ Int, 2018. \u003cstrong\u003e117\u003c/strong\u003e: p. 154-163.\u003c/li\u003e\n\u003cli\u003eLevey, A.S., et al., \u003cem\u003eA more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.\u003c/em\u003e Ann Intern Med, 1999. \u003cstrong\u003e130\u003c/strong\u003e(6): p. 461-70.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024.\u003c/em\u003e Diabetes Care, 2024. \u003cstrong\u003e47\u003c/strong\u003e(Suppl 1): p. S20-s42.\u003c/li\u003e\n\u003cli\u003eSeitz, A.E., M.S. Eberhardt, and S.L. Lukacs, \u003cem\u003eAnemia Prevalence and Trends in Adults Aged 65 and Older: U.S. National Health and Nutrition Examination Survey: 2001-2004 to 2013-2016.\u003c/em\u003e J Am Geriatr Soc, 2018. \u003cstrong\u003e66\u003c/strong\u003e(12): p. 2431-2432.\u003c/li\u003e\n\u003cli\u003eCai, W., et al., \u003cem\u003eAssociation between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database.\u003c/em\u003e Cardiovasc Diabetol, 2023. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 138.\u003c/li\u003e\n\u003cli\u003eZhang, R., et al., \u003cem\u003eIndependent effects of the triglyceride-glucose index on all-cause mortality in critically ill patients with coronary heart disease: analysis of the MIMIC-III database.\u003c/em\u003e Cardiovasc Diabetol, 2023. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 10.\u003c/li\u003e\n\u003cli\u003eZheng, R., et al., \u003cem\u003eAssociation between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database.\u003c/em\u003e Cardiovasc Diabetol, 2023. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 307.\u003c/li\u003e\n\u003cli\u003eBoshen, Y., et al., \u003cem\u003eTriglyceride-glucose index is associated with the occurrence and prognosis of cardiac arrest: a multicenter retrospective observational study.\u003c/em\u003e Cardiovasc Diabetol, 2023. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 190.\u003c/li\u003e\n\u003cli\u003eGuo, J., et al., \u003cem\u003eThe triglycerides-glucose index and the triglycerides to high-density lipoprotein cholesterol ratio are both effective predictors of in-hospital death in non-diabetic patients with AMI.\u003c/em\u003e PeerJ, 2022. \u003cstrong\u003e10\u003c/strong\u003e: p. e14346.\u003c/li\u003e\n\u003cli\u003eCheng, L., et al., \u003cem\u003eAssociation of dynamic change of triglyceride-glucose index during hospital stay with all-cause mortality in critically ill patients: a retrospective cohort study from MIMIC IV2.0.\u003c/em\u003e Cardiovasc Diabetol, 2023. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 142.\u003c/li\u003e\n\u003cli\u003eAlavi Tabatabaei, G., et al., \u003cem\u003eAssociation of the triglyceride glucose index with all-cause and cardiovascular mortality in a general population of Iranian adults.\u003c/em\u003e Cardiovasc Diabetol, 2024. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 66.\u003c/li\u003e\n\u003cli\u003eLaakso, M. and J. Kuusisto, \u003cem\u003eInsulin resistance and hyperglycaemia in cardiovascular disease development.\u003c/em\u003e Nat Rev Endocrinol, 2014. \u003cstrong\u003e10\u003c/strong\u003e(5): p. 293-302.\u003c/li\u003e\n\u003cli\u003eOrmazabal, V., et al., \u003cem\u003eAssociation between insulin resistance and the development of cardiovascular disease.\u003c/em\u003e Cardiovasc Diabetol, 2018. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 122.\u003c/li\u003e\n\u003cli\u003eShulman, G.I., \u003cem\u003eEctopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease.\u003c/em\u003e N Engl J Med, 2014. \u003cstrong\u003e371\u003c/strong\u003e(23): p. 2237-8.\u003c/li\u003e\n\u003cli\u003eHedayatnia, M., et al., \u003cem\u003eDyslipidemia and cardiovascular disease risk among the MASHAD study population.\u003c/em\u003e Lipids Health Dis, 2020. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 42.\u003c/li\u003e\n\u003cli\u003eLi, M., et al., \u003cem\u003eTrends in insulin resistance: insights into mechanisms and therapeutic strategy.\u003c/em\u003e Signal Transduct Target Ther, 2022. \u003cstrong\u003e7\u003c/strong\u003e(1): p. 216.\u003c/li\u003e\n\u003cli\u003eCho, K.H., et al., \u003cem\u003eLow-density lipoprotein cholesterol level in patients with acute myocardial infarction having percutaneous coronary intervention (the cholesterol paradox).\u003c/em\u003e Am J Cardiol, 2010. \u003cstrong\u003e106\u003c/strong\u003e(8): p. 1061-8.\u003c/li\u003e\n\u003cli\u003eReddy, V.S., et al., \u003cem\u003eRelationship between serum low-density lipoprotein cholesterol and in-hospital mortality following acute myocardial infarction (the lipid paradox).\u003c/em\u003e Am J Cardiol, 2015. \u003cstrong\u003e115\u003c/strong\u003e(5): p. 557-62.\u003c/li\u003e\n\u003cli\u003eNavarese, E.P., et al., \u003cem\u003eAssociation Between Baseline LDL-C Level and Total and Cardiovascular Mortality After LDL-C Lowering: A Systematic Review and Meta-analysis.\u003c/em\u003e Jama, 2018. \u003cstrong\u003e319\u003c/strong\u003e(15): p. 1566-1579.\u003c/li\u003e\n\u003cli\u003eWu, M., et al., \u003cem\u003eAssociation of low-density lipoprotein-cholesterol with all-cause and cause-specific mortality.\u003c/em\u003e Diabetes Metab Syndr, 2023. \u003cstrong\u003e17\u003c/strong\u003e(6): p. 102784.\u003c/li\u003e\n\u003cli\u003eKovesdy, C.P., J.E. Anderson, and K. Kalantar-Zadeh, \u003cem\u003eInverse association between lipid levels and mortality in men with chronic kidney disease who are not yet on dialysis: effects of case mix and the malnutrition-inflammation-cachexia syndrome.\u003c/em\u003e J Am Soc Nephrol, 2007. \u003cstrong\u003e18\u003c/strong\u003e(1): p. 304-11.\u003c/li\u003e\n\u003cli\u003eTian, X., et al., \u003cem\u003eTime course of the triglyceride glucose index accumulation with the risk of cardiovascular disease and all-cause mortality.\u003c/em\u003e Cardiovasc Diabetol, 2022. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 183.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eExecutive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III).\u003c/em\u003e Jama, 2001. \u003cstrong\u003e285\u003c/strong\u003e(19): p. 2486-97.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable1. Baseline characteristics of the study population according to TyG index and LDL-C.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1082\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.960295475530934%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"42.75161588180979%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eLDL-C＜2.6 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3850415512465375%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.51800554016621%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eLDL-C≥2.6 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3850415512465375%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e54.06±0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e61.30±0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e59.17±0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e54.98±0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e57.45±0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.71±0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e57.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e58.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e57.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e49.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; non-Hispanic black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;9.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; non-Hispanic white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e62.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e70.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e69.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e60.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e66.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eSmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e50.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e34.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eAlcohol drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e74.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e80.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e76.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e80.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e83.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; heavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e16.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsurance, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; not covered any insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; other insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; private insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e60.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e60.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e62.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e58.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e55.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003ePoverty index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;1.3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; 1.30-2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; ≥3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e41.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; below high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e16.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; college or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e53.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e50.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eMatrimony, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; never married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003edivorced/separated/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; married/living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e61.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e56.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; sedentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e39.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; vigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e123.20±0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e128.18±0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e130.71±0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e128.89±0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e130.72±0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e131.55±0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e66.41±0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.94±0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e71.36±0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e70.78±0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e72.40±0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e73.21±0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eBMI, kg.m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.47±0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.70±0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.16±0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.94±0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.89±0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.44±0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eLaboratory indexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; HbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.66±0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.11±0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.04±0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.65±0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.99±0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.11±0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; NLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.54±0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.46±0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.45±0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.36±0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.32±0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.28±0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; WBC, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.60±0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.22±0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.78±0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.71±0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.30±0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.56±0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; total cholesterol, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.92±0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.97±0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.20±0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.33±0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.53±0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.92±0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; HDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.62±0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.33±0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.11±0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.54±0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.33±0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.16±0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; BUN, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.41±0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.71±0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.72±0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.20±0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.41±0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.28±0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; uric acid, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e316.97±3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e347.40±4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e356.92±4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e332.44±2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e354.90±3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e354.57±3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003euACR, mg/g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e78.04±15.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e65.46±7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e111.76±21.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e69.02±7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e73.21±6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e135.69±22.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; eGFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e88.09±1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e80.23±1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e83.47±1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e86.88±0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e84.81±0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e87.77±0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eMedical history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; CVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; anemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; HT, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e47.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e62.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e69.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e47.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e54.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e52.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; CKD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e58.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e60.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; DM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e54.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e81.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e48.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e70.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003eDrug history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;glucose-lowering drug, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e57.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;antihypertensive drug, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e50.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e73.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e74.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e50.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.97966728280961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; lipid-lowering drug, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.820702402957487%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e64.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6709796672828094%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78003696857671%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519408502772643%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.377079482439926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.3863216266173752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are means ± SD, median (interquartile range), or n (%)\u003c/p\u003e\n\u003cp\u003eAbbreviation: TyG,\u0026nbsp;triglyceride-glucose; LDL-C, low-density lipoprotein cholesterol;\u0026nbsp;SBP,\u0026nbsp;systolic blood pressure;\u0026nbsp;DBP, diastolic blood pressure; BMI, body mass index; HbA1c, glucated hemoglobin; NLR, neutrophil/lymphocyte ratio; WBC,\u0026nbsp;white blood cell; HDL-C, high-density lipoprotein cholesterol;\u0026nbsp;BUN,\u0026nbsp;blood urea nitrogen; uACR, urea albumin creatinine ratio; eGFR, estimated glomerular filtration rate;\u0026nbsp;CVD, cardiovascular disease;\u0026nbsp;HT, hypertension; CKD, chronic kidney disease; DM, diabetes mellitus; T, tristile.\u003c/p\u003e\n\u003cp\u003eTable 2. Risk of all-cause and cardiovascular mortality upon co-exposure stratified by the TyG index and LDL-C.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"888\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.817361894024803%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.905298759864714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.043968432919955%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"39.23337091319053%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCardiovascular mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.817361894024803%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.72942502818489%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.17587373167982%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.043968432919955%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.140924464487036%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.092446448703495%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.817361894024803%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C ≥2.6 \u0026amp; TyG T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.72942502818489%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.140924464487036%\" valign=\"top\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.817361894024803%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C ≥2.6 \u0026amp; TyG T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.72942502818489%\" valign=\"top\"\u003e\n \u003cp\u003e1.06(0.83,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.17587373167982%\" valign=\"top\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.140924464487036%\" valign=\"top\"\u003e\n \u003cp\u003e1.05(0.69,1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.092446448703495%\" valign=\"top\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.817361894024803%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C ≥2.6 \u0026amp; TyG T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.72942502818489%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.47(1.15,1.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.17587373167982%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.140924464487036%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.65(1.09,2.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.092446448703495%\" 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=\"25.817361894024803%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C \u0026lt;2.6 \u0026amp; TyG T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.72942502818489%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.59(1.22,2.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.17587373167982%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.043968432919955%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.140924464487036%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.64(1.04,2.58)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.092446448703495%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.817361894024803%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C \u0026lt;2.6 \u0026amp; TyG T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.72942502818489%\" valign=\"top\"\u003e\n \u003cp\u003e1.24(0.93,1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.17587373167982%\" valign=\"top\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.043968432919955%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.140924464487036%\" valign=\"top\"\u003e\n \u003cp\u003e1.35(0.88,2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.092446448703495%\" valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.817361894024803%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C \u0026lt;2.6 \u0026amp; TyG T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.72942502818489%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.33(1.00,1.75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.17587373167982%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.043968432919955%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.140924464487036%\" valign=\"top\"\u003e\n \u003cp\u003e1.29(0.83,1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.092446448703495%\" valign=\"top\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel adjusted for age, gender, race, alcohol drinking, smoking, physical activity, poverty index, education, matrimony, insurance, body mass index, hypertension, diabetes, chronic kidney disease, cardiovascular disease, anemia, neutrophil/lymphocyte ratio, high-density lipoprotein cholesterol, uric acid, blood urea nitrogen, glucose-lowering drug, antihypertensive drug, and lipid-lowering drug.\u003c/p\u003e\n\u003cp\u003eAbbreviation: TyG, triglyceride-glucose; LDL-C, low-density lipoprotein cholesterol; T, tristile; HR, hazard ratio.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardio-renal-metabolic disease, All-cause mortality, Cardiovascular mortality, Low-density lipoprotein cholesterol, Triglyceride-glucose index","lastPublishedDoi":"10.21203/rs.3.rs-4890377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4890377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Both the triglyceride-glucose (TyG) index, as a surrogate marker of insulin resistance, and low-density lipoprotein cholesterol (LDL-C) are independent risk factors for long-term prognosis among patients with cardio-renal-metabolic (CRM) disease. However, the co-exposures of TyG index and LDL-C to all-cause and cardiovascular\u003cstrong\u003e \u003c/strong\u003edeath among patients with CRM remain unknown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003ePatients with CRM from the National Health and Nutrition Examination Survey (NHANES) database (1999–2018) were included. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Multivariable Cox and restricted cubic spline (RCS) regression models were used to estimate the individual and joint association of TyG index and LDL-C with the risk of all-cause and cardiovascular mortality. The interaction between the TyG index and LDL-C to mortality was also evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDuring a median follow-up of 7.6 years, there were 1608 (22.8%) and 609 (8.4%) patients who died from all-cause and cardiovascular mortality, respectively. In patients with LDL-C\u0026lt;2.6 mmol/L, no significant differences were observed in all-cause and cardiovascular mortality when comparing higher TyG index to the lowest tertile (T1). Specifically, the hazard ratios (HRs) for all-cause mortality in the second (T2) and third tertiles (T3) were 0.81 (95%CI: 0.59–1.09) and 0.87 (95%CI: 0.62–1.22), respectively, with a P for trend of 0.468. For cardiovascular mortality, the HRs for T2 and T3 compared to T1 were 0.80 (95%CI: 0.48–1.32) and 0.72 (95%CI: 0.45–1.15), respectively, with a P for trend of 0.173. However, elevated TyG index was related to markedly increased risk of all-cause and cardiovascular mortality in patients with LDL-C≥2.6 mmol/L. Specifically, for all-cause mortality, HRs for T2 and T3 compared to T1 were 1.01 (95%CI: 0.79–1.28) and 1.38 (95%CI: 1.07–1.79), respectively, with a P for trend of 0.009. For cardiovascular mortality, the HRs were 1.09 (95% CI: 0.72–1.65) for T2 and 1.80 (95% CI: 1.18–2.75) for T3, with a P for trend of 0.005. Similar results were found in RCS and sensitivity analyses. Interactive analysis also demonstrated that a significant association of TyG index and LDL-C with the risk of all-cause (P for interaction=0.011) and cardiovascular (P for interaction=0.050) mortality was observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur research demonstrated the co-exposure effects between the TyG index and LDL-C on all-cause and cardiovascular mortality. Elevated TyG index can significantly increase the risk of all-cause and cardiovascular mortality only among CRM patients with LDL-C ≥ 2.6 mmol/L, but not among patients with LDL-C \u0026lt; 2.6 mmol/L.\u003c/p\u003e","manuscriptTitle":"Joint association of TyG index and LDL-C with all-cause and cardiovascular mortality among patients with cardio-renal-metabolic disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-07 14:06:18","doi":"10.21203/rs.3.rs-4890377/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-05T16:14:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-04T01:28:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134794925614890562077050557055888636981","date":"2024-11-03T14:12:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-02T19:58:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-20T02:28:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215905752991836027476518723586588442915","date":"2024-10-20T00:25:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292521896886351613671952360930005264896","date":"2024-10-17T01:54:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207402179162020723559579460323530360303","date":"2024-10-13T18:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6863959595311138625416225390386281977","date":"2024-10-11T16:43:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-23T10:13:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-23T10:11:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-13T03:57:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-10T07:06:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-10T06:54:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a90a4b18-66d8-4601-ba6b-0697a29f180b","owner":[],"postedDate":"October 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36710202,"name":"Health sciences/Risk factors"},{"id":36710203,"name":"Health sciences/Endocrinology"},{"id":36710204,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Dyslipidaemias"},{"id":36710205,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":36710206,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"}],"tags":[],"updatedAt":"2025-02-24T16:54:31+00:00","versionOfRecord":{"articleIdentity":"rs-4890377","link":"https://doi.org/10.1038/s41598-025-87416-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-18 15:57:00","publishedOnDateReadable":"February 18th, 2025"},"versionCreatedAt":"2024-10-07 14:06:18","video":"","vorDoi":"10.1038/s41598-025-87416-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-87416-7","workflowStages":[]},"version":"v1","identity":"rs-4890377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4890377","identity":"rs-4890377","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}