The correlation between LDL-C/HDL-C and Hypertension: a case control study

The correlation between LDL-C/HDL-C and Hypertension: a case control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The correlation between LDL-C/HDL-C and Hypertension: a case control study Jianling Zhang, Gang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2292912/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dyslipidemia is a risk factor of hypertension, which can promote the occurrence and development of hypertension. In this study, we collect data of 362 elderly patients to explore the correlation between blood lipid indexes and hypertension in the elderly population. Patients were divided into two groups : hypertensive group and control group. Multivariate logistic regression analysis, Spearman correlation analysis and receiver operating characteristics (ROC) curve were used to analyze the correlation between blood lipid and hypertension and its predictive value for hypertension. We found that The levels of LDL-C/HDL-C in hypertensive group were higher than those in control group, and the levels of LDL-C/HDL-C in moderate and severe hypertension groups were higher than those in mild hypertension group (P < 0.05). Multivariate logistic regression analysis showed that LDL-C/HDL-C was an independent risk factor for hypertension. Correlation analysis showed that LDL-C/HDL-C was positively correlated with the severity of hypertension (r = 0.580, P < 0. 01). ROC curve analysis showed that area under the curve (AUC) of LDL-C/HDL-C in predicting hypertension was 0. 937 (95%CI: 0.914-0. 961, P < 0.01). When the cut-off value was 4.276, the specificity and sensitivity were 96.2% and 75. 20%. In conclusion, LDL-C/HDL-C is an independent risk factor for hypertension and has predictive value for hypertension. Health sciences/Cardiology Health sciences/Diseases Health sciences/Health care Health sciences/Risk factors Figures Figure 1 Introduction Dyslipidemia and hypertension are two independent risk factors for cardiovascular diseases, and they often coexist 1 . A survey on hypertension combined with cardiovascular disease risk factors showed that 81.2% of patients with hypertension combined with at least one dyslipidemia and 61% with hypercholesterolemia 2 . Studies have shown that hypertension combined with dyslipidemia will further increase the risk of cardiovascular diseases and death, and there are limitations in reducing cardiovascular and cerebrovascular diseases in hypertensive patients 3 . Studies have found that dyslipidemia is also a risk factor for hypertension. People with dyslipidemia can double the risk of hypertension 4 . Therefore, early identification and intervention of abnormal blood lipids is an important means to prevent and delay hypertension and cardiovascular diseases. At present, most clinical studies are limited to the correlation between a single lipid index and hypertension 5,6 . While studies on the correlation between lipid ratio and hypertension are limited and mostly concentrated in European and American countries. Studies have shown that compared with a single lipid index, lipid ratio can better assess the risk of hypertension 7 . In this study, we discuss the correlation between blood lipid indexes and hypertension in the elderly population, in order to provide new ideas for the early prevention of hypertension in the elderly. Materials And Methods Study population This study included 362 subjects who were hospitalized in Hebei General Hospital from January 2021 to December 2021: 258 patients with hypertension, and 104 controls. In order to ensure the effective implementation of the experiment, all subjects with a history or evidence of cardiovascular and cerebrovascular diseases, renovascular hypertension, renal parenchymal hypertension, primary hyperaldosteronism, Cushing syndrome, pheochromocytoma, hyperthyroidism, abnormal liver and kidney function and using lipid-lowering and blood-pressure medications were excluded from the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by Hebei General Hospital. All participants signed written informed consent. Determination Of Hypertension Hypertension grades was defined according to 2018 ESC/ESH Guidelines for the management of arterial hypertension 8 . Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg; Mild hypertension was defined as SBP 140–159 mmHg and/or DBP 90–99 mmHg; Moderate hypertension was defined as SBP 160–179 mmHg and/or DBP 100–109 mmHg; Severe hypertension was defined as SBP ≥ 180 mmHg and/or DBP ≥ 110 mmHg. Anthropometric And Laboratory Assessment We collect and record the following patient data through the inpatient electronic medical record system: age, gender, smoking history (defined as continuous or cumulative smoking for 6 months or more) 9 and drinking history (defined as drinking at least once per week, regardless of the type of drinking), diabetes history 9 ; The height and weight of the patients were measured by medical height and Body mass meter, and the Body mass index (BMI) was calculated. Electronic sphygmomanometer was used to measure the sitting blood pressure of the patients. Before the first measurement, the patients were told to rest for at least 5 minutes. The measurement was made for three times with a 2-minute interval for each measurement. Finally, the average value of the last two measurements is selected as the final measurement value. We told the patient to fast for 8 hours at night and draw 3–4 mL of elbow venous blood the next morning. We put the patient's venous blood in an anticoagulant tube for testing. Automatic biochemical analyzer is used to measure total cholesterol (TC), triglyceride (TG), LDL-C (Low density lipoprotein-cholesterol), HDL-C (High density lipoprotein-cholesterol), apolipoprotein A1, Apolipoprotein B, lipoprotein a, and calculate LDL-C/HDL-C. Statistical analysis SPSS 25.0 statistical software was used for data analysis. Continuous variables with non-normal distribution were represented as M (P25-P75), and comparison between groups was performed by Mann-Whitney U test. Chi-square test was used for categorical variables, which were represented by case number and percentage n (%). Multivariate logistic regression analysis was used to explore the blood lipid indexes affecting hypertension. Odds Ratios (OR) and 95% confidence intervals were used to estimate the association between blood lipid and hypertension. Spearman correlation tests was used to explore the correlation between blood lipids and grades of hypertension. ROC curve was used to evaluate the predictive value of blood lipid for hypertension in the elderly. P < 0.05 indicated statistically significant difference. Results Comparison of clinical data of the studied populations The finding of clinical data are given in Table 1 . Diabetes revealed significant difference in hypertension patients and controls. While age, sex, BMI, smoking history, drinking history displayed no significant change between two groups. TG, LDL-C, LDL-C/HDL-C, apolipoprotein B, lipoprotein a were elevated in patients with hypertension, and HDL-C was reduced (P＜0.05). While TC and ApolipoproteinA1 showed no significant difference between two groups. Table 1 Comparison of clinical data of the studied populations Variables Hypertension N = 258 Control N = 104 P Value Demographic Characteristic Age((year) M (P25,P75) 70 (65,75) 69 (65,75) 0.914 Male,n (%) 129 (53.8) 56 (50.0) 0.508 BMI (kg/m 2 ) M (P25,P75) 25.07 (22.88,27.27) 24.39 (21.88,26.71) 0.071 Smoking history,n (%) 87 (33.7) 28 (26.9) 0.209 Drinking history,n (%) 141 (54.7) 50 (48.1) 0.257 History of diabetes,n (%) 138 (53.5) 38 (36.5) 0.004 Blood lipid Indexes TC (mmol/L) M (P25,P75) 5.13 (4.26,5.68) 4.92 (4.06,5.67) 0.799 TG (mmol/L) M (P25,P75) 4.23 (2.52,5.22) 3.62 (2.22,4.48) 0.001 LDL-C (mmol/L) M (P25,P75) 4.20 (3.38,4.84) 3.7 (2.58,4.31) ＜0.001 HDL-C (mmol/L) M (P25,P75) 0.73 (0.59,0.87) 1.13 (1.07,1.22) ＜0.001 LDL-C/HDL-C M (P25,P75) 5.56 (3.93,7.08) 3.19 (2.36,3.97) ＜0.001 ApolipoproteinA1 (mmol/L) M (P25,P75) 1.15 (0.92,1.34) 1.12 (0.89,1.28) 0.186 Apolipoprotein B (mmol/L) M (P25,P75) 0.80(0.66,0.95) 0.72(0.57,0.83) ＜0.001 Lipoprotein a (mmol/L) M (P25,P75) 360.25 (308.73,455.08) 155.9 (81.6,231.2) ＜0.001 Associations: BMI Body mass index, TC Total cholesterol, TG Triglyceride, HDL-C High-density lipoprotein cholesterol, LDL-C Low-density lipoprotein cholesterol. Comparison Of Different Hypertension Grades And Lipid Indexes Compared with mild hypertension, LDL-C/HDL-C, apolipoprotein B, and lipoprotein a were significantly increased and HDL-C was significantly decreased in moderate hypertension and severe hypertension ( P < 0. 001). TG in severe hypertension was significantly increased, not observed in moderate hypertension. Compared with moderate hypertension, LDL-C/HDL-C was significantly increased and HDL-C was significantly decreased in severe hypertension (Table 2 P < 0. 001), and there were no differences in TG, apolipoprotein B, and lipoprotein a between two groups. In addition, there were no significant difference in TC, LDL-C, ApolipoproteinA1 among three groups. Table 2 comparison of different hypertension grades and lipid indexes. Variable Mild hypertension N = 92 Moderate hypertensin N = 88 Severe hypertension N = 78 P Value TC (mmol/L) 5.05 (4.23,5.73) 5.29 (4.38,5.74) 5.07 (4.20,5.54) 0.188 TG (mmol/L) 3.67 (2.23,4.87) 4.49 (3.34,5.45) a 4.23 (2.46,5.12) 0.022 LDL-C (mmol/L) 3.84 (3.23,4.77) 4.27 (3.63,5.01) 4.25 (3.43,4.78) 0.159 HDL-C (mmol/L) 0.92 (0.86,0.96) 0.73 (0.65,0.76) a 0.56 (0.52,0.59) ab ＜0.001 LDL-C/HDL-C 4.23 (3.54,5.31) 6.14 (4.95,6.92) a 7.63 (5.51,8.85) ab ＜0.001 ApolipoproteinA1 (mmol/L) 1.11 (0.91,1.31) 1.19 (0.92,1.38) 1.13 (0.95,1.38) 0.511 Apolipoprotein B (mmol/L) 0.73 (0.58,0.81) 0.82 (0.70,0.98) a 0.90 (0.75,1.07) a ＜0.001 Lipoprotein a (mmol/L) 331.15 (310.37,349.13) 415.70 (294.38,459.8) a 478 (309.58,574.18) a ＜0.001 Data is represented by M(P25,P75) Associations: TC Total cholesterol, TG Triglyceride, HDL-C High-density lipoprotein cholesterol, LDL-C, Low-density lipoprotein cholesterol. a P<0.05 indicates statistically significant differences patients with hypertension moderate and serve hypertension compared with mild hypertension. b P<0.05 indicates statistically significant differences in lipid indices between patients with serve hypertension compared with the serve hypertension. Multi-factor logistic regression analysis and spearman correlation analysis. The study further explored the relationship between lipid indexes and the incidence of hypertension. Multivariate logistic regression analysis showed that only LDL-C/HDL-C and lipoprotein a were independent risk factors for hypertension (OR = 7.070, 95%CI 1.450–9.478 vs OR = 1.031 95%CI 1.019–1.043) ( P < 0.05, Table 3 ) . Spearman correlation analysis was further used to analysis the correlation between blood lipids and the Severity of hypertension. The result showed that LDL-C/HDL-C, lipoprotein a were positively correlated with the severity of hypertension (r = 0.573 vs r = 0.356, P < 0.01). Table 3 Multivariate logistic regression analysis. B SE OR 95%CI P Value TC 2.046 1.875 0.129 0.003 ~ 5.103 0.275 TG 0.233 0.415 0.792 0.351 ~ 1.787 0.574 LDL-C 3.092 2.975 0.045 0.001 ~ 15.475 0.299 HDL-C -1.82 7.687 6.170 0.001 ~ 3.920 0.813 LDL-C/HDL-C 4.763 2.240 7.070 1.450 ~ 9.478 0.034 Apolipoprotein A1 0.263 1.146 1.301 0.138 ~ 12.297 0.818 Apolipoprotein B 0.414 2.079 1.512 0.026 ~ 9.033 0.842 lipoprotein a 0.031 0.006 1.031 1.019 ~ 1.043 ＜0.001 Associations TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol. The Predictive Value Of Ldl-c And Lipoprotein A To Hypertension By comparing the AUC of ROC curve, we can determine the lipid index which is more conducive to predicting hypertension. The AUC of LDL-C/HDL-C was 0.937 (95%CI 0.914–0.961)( Figure. 1a ), and which of the lipoprotein a was 0.933 (95%CI 0.908–0.959, P < 0.001) (Figure. 1b). In addition, when the LDL-C/HDL-C cut-off value was 4.276, the specificity and sensitivity of predicting hypertension were 96.2% and 75.20%. Therefore, this study showed that LDL-C/HDL-C has predictive value for hypertension better than lipoprotein a . Discussion With the rapid development of Chinese society and economy, the degree of population aging has gradually increased. Hypertension and dyslipidemia have become important factors affecting the life process of the elderly 10 . Previous cross-sectional studies have found a biological correlation between dyslipidemia and hypertension 11 , 12 ,and dyslipidemia can increase the incidence of hypertension in people with normal blood pressure 13 , 14 . A randomized controlled trial showed that patients with hypertension had higher lipid levels. Some lipid indexes had good predictive value for hypertension 15 . At present, most studies discuss the correlation between single blood lipids and hypertension, and relatively few studies on the relationship between blood lipid ratio and hypertension. A cross-sectional study in 2019 explored the predictive value of different blood lipids to hypertension. The study found that, compared with single blood lipids, the ratio of lipids can better reflect the comprehensive level of lipid metabolism and had higher clinical application value 16 . At present, a large number of studies have shown that LDL-C/HDL-C ratio is a marker to predict the progression of coronary heart disease and cardiovascular events 17 , 18 , but there are few studies on the relationship between LDL-C/HDL-C ratio and hypertension. In this study, we included LDL-C/HDL-C into the category of blood lipid research to explore its effects to hypertension. Through this study, we found that patients with hypertension had higher lipid levels, including TG, LDL-C, LDL-C/HDL-C, apolipoprotein B and lipoprotein a besides TC and ApolipoproteinA1. However, a recent large community study found a positive correlation between TC levels and SBP in the general population and patients with hypertension. After adjusting relevant variables, it found that the SBP could increase by 0.044 mmHg for every 1 standard deviation increase in TC 5 , which was inconsistent with our research conclusion. The study further explored the relationship between blood lipids and hypertension severity. The results showed that, compared with mild hypertension, LDL-C/HDL-C and lipoprotein a were higher in moderate and severe hypertension. In addition, compared with moderate hypertension, LDL-C/HDL-C and lipoprotein a ware significantly increased in severe hypertension. Further multivariate logistic regression analysis showed that only LDL-C/HDL-C and lipoprotein a were independent risk factors for hypertension in the elderly population, and both were positively correlated with the severity of hypertension. The AUC of LDL-C/HDL-C was 0.937, higher than that of lipoprotein a (AUC = 0.933), and when LDL-C/HDL-C is above 4.276, the risk of hypertension increases. Therefore, LDL-C/HDL-C may be an ideal lipid index for predicting hypertension in the elderly. Foreign studies 19 have also found that blood pressure changes in the elderly are strongly correlated with LDL-C/HDL-C, suggesting that LDL-C/HDL-C can more accurately predict the important role of insulin resistance in the pathogenesis of hypertension. Increased LDL-C/HDL-C ratio leads to increased insulin resistance and secondary hyperinsulinemia. The latter leads to increased reabsorption of water and sodium by the kidneys, activating the sympathetic nervous system and reducing the elasticity of the arteries, thus contributing to higher blood pressure. In addition, A prospective cohort study by Yu Yu et al 20 . showed that the LDL-C/HDL-C ratio could be a valuable predictor of all-cause mortality in elderly patients with hypertension. For elderly patients with hypertension, the LDL-C/HDL-C ratio may be one of the targets of lipid-lowering therapy. In our study, we did not found the correlation between LDL-C ,TG and hypertension. There are some inconsistent research conclusions. A prospective population-based study in Italy reported a significant linear relationship between LDL-C concentration and hypertension among participants who did not receive antihypertensive or lipid-lowering therapy. Even in patients with well-controlled blood pressure, the risk of cardiovascular disease was gradually increased with the increase of LDL-C level 21 . A Finnish cohort study found that TG was an independent influencing factor for hypertension, and the risk of hypertension increased by 1.6 times for every 1 standard deviation increase of TG 22 . Another Spanish study also found that the risk of hypertension in men increased by 47% and 73% when their TG levels were up to one quarter and one fifth respectively 23 . In addition, there are also studies pointed that relationship between TG/HDL-C and hypertension is stronger than other single lipids and lipid ratios, such as TC/HDL-C and LDL-C/HD-C. The risk of hypertension in men with TG/HDL-C was nearly 2 times that of those with low levels, and which in women was 1.48 times 7 . A prospective cohort study by Tohidi 24 found that TG, TC/HDL-C and TG/HDL-C could all predict the risk of hypertension, but TG and TG/HDL-C had better predictive efficacy. Although the current studies on lipids and hypertension have not reached a unified conclusion, some studies have found that the longitudinal correlation between blood pressure and lipids may be related to the impaired endothelial function, the activated of renin angiotensin system, and other common pathological mechanisms 25 . The specific manifestations are as follows: (1) Impaired endothelial function decreased production, release and activity of nitric oxide, decreased vasodilatory ability and increased blood pressure. Secondly, nitric oxide increase sensitivity of vascular endothelium to salt, which affect blood pressure regulation. Third, impaired endothelial function can cause atherosclerosis, increase arterial stiffness and reduce compliance, which lead to hypertension.(2) Hyperinsulinemia caused by insulin resistance is also a potential causative factor of hypertension. On the one hand, it increased blood pressure by over-activating the sympathetic nervous system and the renin angiotensin system 26 , 27 . On the other hand, lipid metabolites such as fatty acids, glycerophospholipids, alanine, aspartic acid and glutamate can also involved in insulin resistance, vascular remodeling and low-density lipoprotein particle oxidation, further promoting blood pressure increase 28 . (3) Renal microcirculation dysfunction caused by hyperlipidemia may also be involved in the development of hypertension 29 .(4) Genetic factors, such as Apolipoprotein (apo) A-I, Apolipoprotein e, microsomal TG transporter and lipoprotein lipase genes may be important genetic factors for hyperlipidemia and hypertension 30 . This study mainly discusses the correlation between LDL-C/HDL-C and hypertension, which is more innovative than the correlation between a single lipid index and hypertension. However, There are some limitations in this study. First of all, the cross-sectional study design was adopted. The author could only find the correlation between blood lipid and hypertension, and the specific pathological mechanism needs further study and elaboration. Secondly, the participants in this study were only from one hospital in mainland China, and the sample size was small. Therefore the conclusions of this study may not be representative, and future prospective cohort studies with larger samples are still needed to be demonstrated. Third, dietary factors may influence lipid levels, but this study did not analyze the influence of this factor due to insufficient dietary data. Fourth, this study only designed the correlation between LDL-C/HDL-C and hypertension, and the relationship between other lipid ratios and blood pressure should be taken into consideration in future studies. Fifth, due to the study design, measurements related biases cannot be excluded. Taken together, this study indicates that LDL-C/HDL-C is an independent risk factor for hypertension and has predictive value for hypertension. It is suggested that medical staff should also pay attention to patients with high LDL-C/HDL-C ratio in the process of screening blood lipid for elderly patients. As early as possible to help patients identify and intervene in dyslipidemia, thus to prevent and delay the occurrence of hypertension and cardiovascular diseases in the elderly. Declarations Data availability Because of data protection, datasets generated and analyzed during the current study are not published, However, if there is a reasonable need, you can contact the corresponding author to obtain it. Acknowledgements We sincerely express our gratitude to Hebei General Hospital for their support of this research. Authors’ contributions All authors met the criteria for authorship. J.Z. designed the study, collected relevant data, and drafted the manuscript. G.L. controlled the critical content of the manuscript and made critical changes. All authors read and approved the final manuscript. All authors read and approved the final manuscript. Funding National Natural Science Foundation of China (81370316) and (81601858). Competing interests The authors declare no conflict of interest to report. Availability of data and materials . The datasets during and/or analyzed during the current study will be available from the corresponding author on reasonable request. Consent for publication No applicable. Additional information Correspondence and requests for materials should be addressed to J.Z and G.L. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations References Tsao, C. W. et al. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association . Circulation 145 ( 8 ), e153–e639 ( 2022 ). Joint Committee for Guideline Revision. 2018 Chinese Guidelines for Prevention and Treatment of Hypertension-A report of the Revision Committee of Chinese Guidelines for Prevention and Treatment of Hypertension. Journal of geriatric cardiology: JGC . 16 (3), 182–241 (2019). Laddu, D. et al. 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Polymorphisms of adiponectin gene and gene-lipid interaction with hypertension risk in Chinese coal miners: A matched case-control study. PloS one. 17 (9), e0268984(2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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-2292912","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":155598818,"identity":"99e0dff7-5f58-4d85-b477-43a72094ff28","order_by":0,"name":"Jianling Zhang","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianling","middleName":"","lastName":"Zhang","suffix":""},{"id":155598819,"identity":"92454612-9ac7-4e0e-9dd0-b09f37080f62","order_by":1,"name":"Gang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACPmYGhgM8EDbjg4SKGsJa2MBaEsBsZoMHZ44RoQVEQLWwST5sYSZCCzvvwQNvf9gkznc/fKwisYGNgb+9O4GAw/gSDs5JSEvceCYt7UbiDhkGiTNnNxDQwmNwmCfhcOLGhhyzG4ln2BgMJHKJ1dL/xqwgsY2ZBC3zJXLMGIjUAvJLWprxBolnyRIJZ47xEPQLP//Zwx/e2NjIzu9PPvjxR0WNHH97L34twEiBUAYHULjEaJFvIELtKBgFo2AUjEwAAOTDSCU2MiSRAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2022-11-20 07:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2292912/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2292912/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30140905,"identity":"b27e93b8-095f-467e-82f8-0f599f3823c2","added_by":"auto","created_at":"2022-12-09 22:39:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77285,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve was used to analyze the predictive value of LDL-C/HDL-C and lipoprotein a to hypertension. The optimal cut-off value refers to the threshold value of the above two indexes for predicting the diagnosis of hypertension. The AUC reflects the prediction strength of the two indexes for hypertension.\u003cstrong\u003e a\u003c/strong\u003e The AUC of LDL-C/HDL-C was 0.937, and the best cut-off value was 4.276. \u003cstrong\u003eb \u003c/strong\u003eThe AUC of lipoprotein a was 0.933, and the best cut-off value was 295.7.\u003c/p\u003e\n\u003cp\u003eP\u0026lt;0.01 indicates that the difference was statistically significant.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2292912/v1/f510a0162950031787b846c5.png"},{"id":37724199,"identity":"008b9716-3e16-44c8-b581-98606ca8eeb9","added_by":"auto","created_at":"2023-05-31 04:59:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":468011,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2292912/v1/cb85d406-f311-4224-9146-aa6d18305909.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The correlation between LDL-C/HDL-C and Hypertension: a case control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDyslipidemia and hypertension are two independent risk factors for cardiovascular diseases,\u0026nbsp;and they often coexist\u003csup\u003e1\u003c/sup\u003e. A survey on \u0026nbsp;hypertension combined with cardiovascular disease risk factors showed that 81.2% of patients with hypertension combined with at least one dyslipidemia and 61% with hypercholesterolemia\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;Studies have shown that hypertension combined with dyslipidemia will further increase the risk of cardiovascular diseases and death,\u0026nbsp;and there are limitations in reducing cardiovascular and cerebrovascular diseases in hypertensive patients\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies have found that dyslipidemia is also a risk factor for hypertension. People with dyslipidemia can double the risk of hypertension\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;Therefore,\u0026nbsp;early identification and intervention of abnormal blood lipids is an important means to prevent and delay hypertension and cardiovascular diseases.\u0026nbsp;At present,\u0026nbsp;most clinical studies are limited to the correlation between a single lipid index and hypertension\u003csup\u003e5,6\u003c/sup\u003e.\u0026nbsp;While studies on the correlation between lipid ratio and hypertension are limited and mostly concentrated in European and American countries.\u0026nbsp;Studies have shown that compared with a single lipid index,\u0026nbsp;lipid ratio can better assess the risk of hypertension\u003csup\u003e7\u003c/sup\u003e. In this study, we discuss the correlation between blood lipid indexes and hypertension in the elderly population, in order to provide new ideas for the early prevention of hypertension in the elderly.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study included 362 subjects who were hospitalized in \u003cem\u003eHebei General Hospital\u003c/em\u003e from January 2021 to December 2021: 258 patients with hypertension, and 104 controls. In order to ensure the effective implementation of the experiment, all subjects with a history or evidence of cardiovascular and cerebrovascular diseases, renovascular hypertension, renal parenchymal hypertension, primary hyperaldosteronism, Cushing syndrome, pheochromocytoma, hyperthyroidism, abnormal liver and kidney function and using lipid-lowering and blood-pressure medications were excluded from the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by Hebei General Hospital. All participants signed written informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDetermination Of Hypertension\u003c/h3\u003e\n\u003cp\u003eHypertension grades was defined according to 2018 ESC/ESH Guidelines for the management of arterial hypertension\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Hypertension was defined as systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg; Mild hypertension was defined as SBP 140\u0026ndash;159 mmHg and/or DBP 90\u0026ndash;99 mmHg; Moderate hypertension was defined as SBP 160\u0026ndash;179 mmHg and/or DBP 100\u0026ndash;109 mmHg; Severe hypertension was defined as SBP\u0026thinsp;\u0026ge;\u0026thinsp;180 mmHg and/or DBP\u0026thinsp;\u0026ge;\u0026thinsp;110 mmHg.\u003c/p\u003e\n\u003ch3\u003eAnthropometric And Laboratory Assessment\u003c/h3\u003e\n\u003cp\u003eWe collect and record the following patient data through the inpatient electronic medical record system: age, gender, smoking history (defined as continuous or cumulative smoking for 6 months or more)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and drinking history (defined as drinking at least once per week, regardless of the type of drinking), diabetes history\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e; The height and weight of the patients were measured by medical height and Body mass meter, and the Body mass index (BMI) was calculated. Electronic sphygmomanometer was used to measure the sitting blood pressure of the patients. Before the first measurement, the patients were told to rest for at least 5 minutes. The measurement was made for three times with a 2-minute interval for each measurement. Finally, the average value of the last two measurements is selected as the final measurement value. We told the patient to fast for 8 hours at night and draw 3\u0026ndash;4 mL of elbow venous blood the next morning. We put the patient's venous blood in an anticoagulant tube for testing. Automatic biochemical analyzer is used to measure total cholesterol (TC), triglyceride (TG), LDL-C (Low density lipoprotein-cholesterol), HDL-C (High density lipoprotein-cholesterol), apolipoprotein A1, Apolipoprotein B, lipoprotein a, and calculate LDL-C/HDL-C.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 25.0 statistical software was used for data analysis. Continuous variables with non-normal distribution were represented as M (P25-P75), and comparison between groups was performed by Mann-Whitney U test. Chi-square test was used for categorical variables, which were represented by case number and percentage n (%). Multivariate logistic regression analysis was used to explore the blood lipid indexes affecting hypertension. Odds Ratios (OR) and 95% confidence intervals were used to estimate the association between blood lipid and hypertension. Spearman correlation tests was used to explore the correlation between blood lipids and grades of hypertension. ROC curve was used to evaluate the predictive value of blood lipid for hypertension in the elderly. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistically significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eComparison of clinical data of the studied populations\u003c/h2\u003e \u003cp\u003eThe finding of clinical data are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Diabetes revealed significant difference in hypertension patients and controls. While age, sex, BMI, smoking history, drinking history displayed no significant change between two groups. TG, LDL-C, LDL-C/HDL-C, apolipoprotein B, lipoprotein a were elevated in patients with hypertension, and HDL-C was reduced (P＜0.05). While TC and ApolipoproteinA1 showed no significant difference between two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical data of the studied populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;258\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;104\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic Characteristic\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAge((year) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (65,75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (65,75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.07 (22.88,27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.39 (21.88,26.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking history,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of diabetes,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood lipid Indexes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTC (mmol/L) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.13 (4.26,5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.92 (4.06,5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23 (2.52,5.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.62 (2.22,4.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20 (3.38,4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.7 (2.58,4.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.59,0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 (1.07,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C/HDL-C M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.56 (3.93,7.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.19 (2.36,3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoproteinA1\u003c/p\u003e \u003cp\u003e(mmol/L) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.92,1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.89,1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein B\u003c/p\u003e \u003cp\u003e(mmol/L) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.66,0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72(0.57,0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein a\u003c/p\u003e \u003cp\u003e(mmol/L) M (P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360.25\u003c/p\u003e \u003cp\u003e(308.73,455.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155.9\u003c/p\u003e \u003cp\u003e(81.6,231.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociations: BMI Body mass index, TC Total cholesterol, TG Triglyceride, HDL-C\u003c/p\u003e \u003cp\u003eHigh-density lipoprotein cholesterol, LDL-C Low-density lipoprotein cholesterol.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison Of Different Hypertension Grades And Lipid Indexes\u003c/h3\u003e\n\u003cp\u003eCompared with mild hypertension, LDL-C/HDL-C, apolipoprotein B, and lipoprotein a were significantly increased and HDL-C was significantly decreased in moderate hypertension and severe hypertension (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0. 001). TG in severe hypertension was significantly increased, not observed in moderate hypertension. Compared with moderate hypertension, LDL-C/HDL-C was significantly increased and HDL-C was significantly decreased in severe hypertension (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0. 001), and there were no differences in TG, apolipoprotein B, and lipoprotein a between two groups. In addition, there were no significant difference in TC, LDL-C, ApolipoproteinA1 among three groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ecomparison of different hypertension grades and lipid indexes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild hypertension\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;92\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate hypertensin\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere hypertension\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;78\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.05 (4.23,5.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.29 (4.38,5.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.07 (4.20,5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.67 (2.23,4.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.49 (3.34,5.45)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.23 (2.46,5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.84 (3.23,4.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.27 (3.63,5.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.25 (3.43,4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.86,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.65,0.76)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56 (0.52,0.59)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C/HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23 (3.54,5.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.14 (4.95,6.92)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.63 (5.51,8.85)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoproteinA1 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e(0.91,1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(0.92,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003cp\u003e(0.95,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein B\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.58,0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(0.70,0.98)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e(0.75,1.07)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein a\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e331.15\u003c/p\u003e \u003cp\u003e(310.37,349.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e415.70\u003c/p\u003e \u003cp\u003e(294.38,459.8)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e478\u003c/p\u003e \u003cp\u003e(309.58,574.18)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData is represented by M(P25,P75) Associations: TC Total cholesterol, TG Triglyceride, HDL-C High-density lipoprotein cholesterol, LDL-C, Low-density lipoprotein cholesterol.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eP\u0026lt;0.05 indicates statistically significant differences patients with hypertension moderate and serve hypertension compared with mild hypertension. \u003csup\u003eb\u003c/sup\u003eP\u0026lt;0.05 indicates statistically significant differences in lipid indices between patients with serve hypertension compared with the serve hypertension.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMulti-factor logistic regression analysis and spearman correlation analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study further explored the relationship between lipid indexes and the incidence of hypertension. Multivariate logistic regression analysis showed that only LDL-C/HDL-C and lipoprotein a were independent risk factors for hypertension (OR\u0026thinsp;=\u0026thinsp;7.070, 95%CI 1.450\u0026ndash;9.478 vs OR\u0026thinsp;=\u0026thinsp;1.031 95%CI 1.019\u0026ndash;1.043) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Spearman correlation analysis was further used to analysis the correlation between blood lipids and the Severity of hypertension. The result showed that LDL-C/HDL-C, lipoprotein a were positively correlated with the severity of hypertension (r\u0026thinsp;=\u0026thinsp;0.573 vs r\u0026thinsp;=\u0026thinsp;0.356, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u0026thinsp;~\u0026thinsp;5.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.351\u0026thinsp;~\u0026thinsp;1.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u0026thinsp;~\u0026thinsp;15.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u0026thinsp;~\u0026thinsp;3.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C/HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.450\u0026thinsp;~\u0026thinsp;9.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u0026thinsp;~\u0026thinsp;12.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u0026thinsp;~\u0026thinsp;9.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elipoprotein a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.019\u0026thinsp;~\u0026thinsp;1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e＜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAssociations\u003c/strong\u003e \u003cp\u003eTC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eThe Predictive Value Of Ldl-c And Lipoprotein A To Hypertension\u003c/h3\u003e\n\u003cp\u003eBy comparing the AUC of ROC curve, we can determine the lipid index which is more conducive to predicting hypertension. The AUC of LDL-C/HDL-C was 0.937 (95%CI 0.914\u0026ndash;0.961)(\u003cb\u003eFigure. 1a\u003c/b\u003e), and which of the lipoprotein a was 0.933 (95%CI 0.908\u0026ndash;0.959, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(Figure. 1b).\u003c/b\u003e In addition, when the LDL-C/HDL-C cut-off value was 4.276, the specificity and sensitivity of predicting hypertension were 96.2% and 75.20%. Therefore, this study showed that LDL-C/HDL-C has predictive value for hypertension better than lipoprotein a .\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith the rapid development of Chinese society and economy, the degree of population aging has gradually increased. Hypertension and dyslipidemia have become important factors affecting the life process of the elderly \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Previous cross-sectional studies have found a biological correlation between dyslipidemia and hypertension\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e,and dyslipidemia can increase the incidence of hypertension in people with normal blood pressure\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A randomized controlled trial showed that patients with hypertension had higher lipid levels. Some lipid indexes had good predictive value for hypertension\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt present, most studies discuss the correlation between single blood lipids and hypertension, and relatively few studies on the relationship between blood lipid ratio and hypertension. A cross-sectional study in 2019 explored the predictive value of different blood lipids to hypertension. The study found that, compared with single blood lipids, the ratio of lipids can better reflect the comprehensive level of lipid metabolism and had higher clinical application value\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. At present, a large number of studies have shown that LDL-C/HDL-C ratio is a marker to predict the progression of coronary heart disease and cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, but there are few studies on the relationship between LDL-C/HDL-C ratio and hypertension. In this study, we included LDL-C/HDL-C into the category of blood lipid research to explore its effects to hypertension.\u003c/p\u003e \u003cp\u003eThrough this study, we found that patients with hypertension had higher lipid levels, including TG, LDL-C, LDL-C/HDL-C, apolipoprotein B and lipoprotein a besides TC and ApolipoproteinA1. However, a recent large community study found a positive correlation between TC levels and SBP in the general population and patients with hypertension. After adjusting relevant variables, it found that the SBP could increase by 0.044 mmHg for every 1 standard deviation increase in TC\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, which was inconsistent with our research conclusion.\u003c/p\u003e \u003cp\u003eThe study further explored the relationship between blood lipids and hypertension severity. The results showed that, compared with mild hypertension, LDL-C/HDL-C and lipoprotein a were higher in moderate and severe hypertension. In addition, compared with moderate hypertension, LDL-C/HDL-C and lipoprotein a ware significantly increased in severe hypertension. Further multivariate logistic regression analysis showed that only LDL-C/HDL-C and lipoprotein a were independent risk factors for hypertension in the elderly population, and both were positively correlated with the severity of hypertension. The AUC of LDL-C/HDL-C was 0.937, higher than that of lipoprotein a (AUC\u0026thinsp;=\u0026thinsp;0.933), and when LDL-C/HDL-C is above 4.276, the risk of hypertension increases. Therefore, LDL-C/HDL-C may be an ideal lipid index for predicting hypertension in the elderly. Foreign studies\u003csup\u003e19\u003c/sup\u003ehave also found that blood pressure changes in the elderly are strongly correlated with LDL-C/HDL-C, suggesting that LDL-C/HDL-C can more accurately predict the important role of insulin resistance in the pathogenesis of hypertension. Increased LDL-C/HDL-C ratio leads to increased insulin resistance and secondary hyperinsulinemia. The latter leads to increased reabsorption of water and sodium by the kidneys, activating the sympathetic nervous system and reducing the elasticity of the arteries, thus contributing to higher blood pressure. In addition, A prospective cohort study by Yu Yu et al\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. showed that the LDL-C/HDL-C ratio could be a valuable predictor of all-cause mortality in elderly patients with hypertension. For elderly patients with hypertension, the LDL-C/HDL-C ratio may be one of the targets of lipid-lowering therapy.\u003c/p\u003e \u003cp\u003eIn our study, we did not found the correlation between LDL-C ,TG and hypertension. There are some inconsistent research conclusions. A prospective population-based study in Italy reported a significant linear relationship between LDL-C concentration and hypertension among participants who did not receive antihypertensive or lipid-lowering therapy. Even in patients with well-controlled blood pressure, the risk of cardiovascular disease was gradually increased with the increase of LDL-C level\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A Finnish cohort study found that TG was an independent influencing factor for hypertension, and the risk of hypertension increased by 1.6 times for every 1 standard deviation increase of TG\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Another Spanish study also found that the risk of hypertension in men increased by 47% and 73% when their TG levels were up to one quarter and one fifth respectively \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, there are also studies pointed that relationship between TG/HDL-C and hypertension is stronger than other single lipids and lipid ratios, such as TC/HDL-C and LDL-C/HD-C. The risk of hypertension in men with TG/HDL-C was nearly 2 times that of those with low levels, and which in women was 1.48 times\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. A prospective cohort study by Tohidi \u003csup\u003e24\u003c/sup\u003efound that TG, TC/HDL-C and TG/HDL-C could all predict the risk of hypertension, but TG and TG/HDL-C had better predictive efficacy.\u003c/p\u003e \u003cp\u003eAlthough the current studies on lipids and hypertension have not reached a unified conclusion, some studies have found that the longitudinal correlation between blood pressure and lipids may be related to the impaired endothelial function, the activated of renin angiotensin system, and other common pathological mechanisms\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The specific manifestations are as follows: (1) Impaired endothelial function decreased production, release and activity of nitric oxide, decreased vasodilatory ability and increased blood pressure. Secondly, nitric oxide increase sensitivity of vascular endothelium to salt, which affect blood pressure regulation. Third, impaired endothelial function can cause atherosclerosis, increase arterial stiffness and reduce compliance, which lead to hypertension.(2) Hyperinsulinemia caused by insulin resistance is also a potential causative factor of hypertension. On the one hand, it increased blood pressure by over-activating the sympathetic nervous system and the renin angiotensin system \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. On the other hand, lipid metabolites such as fatty acids, glycerophospholipids, alanine, aspartic acid and glutamate can also involved in insulin resistance, vascular remodeling and low-density lipoprotein particle oxidation, further promoting blood pressure increase\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. (3) Renal microcirculation dysfunction caused by hyperlipidemia may also be involved in the development of hypertension \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.(4) Genetic factors, such as Apolipoprotein (apo) A-I, Apolipoprotein e, microsomal TG transporter and lipoprotein lipase genes may be important genetic factors for hyperlipidemia and hypertension\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study mainly discusses the correlation between LDL-C/HDL-C and hypertension, which is more innovative than the correlation between a single lipid index and hypertension. However, There are some limitations in this study. First of all, the cross-sectional study design was adopted. The author could only find the correlation between blood lipid and hypertension, and the specific pathological mechanism needs further study and elaboration. Secondly, the participants in this study were only from one hospital in mainland China, and the sample size was small. Therefore the conclusions of this study may not be representative, and future prospective cohort studies with larger samples are still needed to be demonstrated. Third, dietary factors may influence lipid levels, but this study did not analyze the influence of this factor due to insufficient dietary data. Fourth, this study only designed the correlation between LDL-C/HDL-C and hypertension, and the relationship between other lipid ratios and blood pressure should be taken into consideration in future studies. Fifth, due to the study design, measurements related biases cannot be excluded.\u003c/p\u003e \u003cp\u003eTaken together, this study indicates that LDL-C/HDL-C is an independent risk factor for hypertension and has predictive value for hypertension. It is suggested that medical staff should also pay attention to patients with high LDL-C/HDL-C ratio in the process of screening blood lipid for elderly patients. As early as possible to help patients identify and intervene in dyslipidemia, thus to prevent and delay the occurrence of hypertension and cardiovascular diseases in the elderly.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause of data protection, datasets generated and analyzed during the current study are not published, However, if there is a reasonable need, you can contact the corresponding author to obtain it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely express our gratitude to\u0026nbsp;Hebei General Hospital\u0026nbsp;for their support of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll\u0026nbsp;authors\u0026nbsp;met\u0026nbsp;the\u0026nbsp;criteria\u0026nbsp;for authorship. J.Z. designed the study, collected relevant data, and drafted the manuscript. G.L. controlled the critical content of the manuscript and made critical changes. All authors read and approved the final manuscript. All authors read and approved the final manuscript. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (81370316) and (81601858).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets during and/or analyzed during the current study will be available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo applicable. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to J.Z and G.L. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReprints and permissions information\u003c/strong\u003e is available at www.nature.com/reprints. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003c/strong\u003e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsao, C. 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Polymorphisms of adiponectin gene and gene-lipid interaction with hypertension risk in Chinese coal miners: A matched case-control study. PloS one. \u003cb\u003e17\u003c/b\u003e(9), e0268984(2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2292912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2292912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDyslipidemia is a risk factor of hypertension, which can promote the occurrence and development of hypertension. In this study, we collect data of 362 elderly patients to explore the correlation between blood lipid indexes and hypertension in the elderly population. Patients were divided into two groups : hypertensive group and control group. Multivariate logistic regression analysis, Spearman correlation analysis and receiver operating characteristics (ROC) curve were used to analyze the correlation between blood lipid and hypertension and its predictive value for hypertension. We found that The levels of LDL-C/HDL-C in hypertensive group were higher than those in control group, and the levels of LDL-C/HDL-C in moderate and severe hypertension groups were higher than those in mild hypertension group (P \u0026lt; 0.05). Multivariate logistic regression analysis showed that LDL-C/HDL-C was an independent risk factor for hypertension. Correlation analysis showed that LDL-C/HDL-C was positively correlated with the severity of hypertension (r = 0.580, P \u0026lt; 0. 01). ROC curve analysis showed that area under the curve (AUC) of LDL-C/HDL-C in predicting hypertension was 0. 937 (95%CI: 0.914-0. 961, P \u0026lt; 0.01). When the cut-off value was 4.276, the specificity and sensitivity were 96.2% and 75. 20%. In conclusion, LDL-C/HDL-C is an independent risk factor for hypertension and has predictive value for hypertension.\u003c/p\u003e","manuscriptTitle":"The correlation between LDL-C/HDL-C and Hypertension: a case control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-09 22:39:20","doi":"10.21203/rs.3.rs-2292912/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40d2c43c-7240-4641-8b98-88352fb4d616","owner":[],"postedDate":"December 9th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17288035,"name":"Health sciences/Cardiology"},{"id":17288036,"name":"Health sciences/Diseases"},{"id":17288037,"name":"Health sciences/Health care"},{"id":17288038,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2023-05-31T04:59:31+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-09 22:39:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2292912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2292912","identity":"rs-2292912","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}