Correlation Analysis of Blood Lipid Level and Thyroid Tumors

Correlation Analysis of Blood Lipid Level and Thyroid Tumors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation Analysis of Blood Lipid Level and Thyroid Tumors Shuang Liu, Yuanyuan Guo, Zhijie Li, Jun Gong, Pu Liao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4108925/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The level of blood lipids has been found to reflect the occurrence and development of tumors to a certain extent. This article explores the correlation between blood lipid levels and thyroid tumors. Methods: A total of 915 patients with thyroid tumors were enrolled in this study and divided into two groups according to the benign and malignant tumors . The total cholesterol (TCHO), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), lipoprotein (a) and other detection indicators before starting treatment were recorded. The comparison between the two groups were evaluated by Mann-Whitney test and Chi-square test. The relationship between the blood lipid level and thyroid tumors were assessed by Spearman correlation analysis , dose-effect analysis and logistic regression methods. Results: Serum TCHO (median 4.85 vs 5,14, p =0.001), HDL-C (median 1.37 vs 1.48, p =0.001), LDL-C (median 2.50 vs 2.68, p =0.006) and lipoprotein (a) (median 111 vs 133, p =0.023) levels of patients with malignant thyroid tumors were significantly lower than those of patients with benign thyroid tumors. The logistic regression models showed that the OR value of HDL-C was 0.493(0.272,0.894), and the P value was 0.02. This suggests that HDL-C is a protective factor. In addition, HDL-C was negatively correlated with lymph node metastasis( p =0.005) and the maximum tumor diameter( p =0.024). Conclusion: Serum lipid level is correlated with the occurrence and development of thyroid tumors to a certain extent, and the level of HDL-C is a protective factor for thyroid tumors. lipids thyroid tumor Figures Figure 1 Figure 2 Introduction Thyroid cancer is the most common malignant tumor of the head and neck, and it originates from the follicular epithelium or parafollicular epithelial cells of the thyroid gland 1 . In 2019, there were 233,846 new cases of thyroid cancer worldwide and 45,575 deaths from thyroid cancer, of which the proportion in China was 16.71% and 15.88% respectively. China has the highest number of thyroid cancer cases and deaths in the world, and the incidence has risen sharply in some provinces 2 . According to the survey of the Chinese Tumor Registry Center, the incidence of thyroid cancer in urban Chinese women ranks fourth among female malignant tumors and will continue to increase by 20% per year 3 . For benign thyroid tumors, most cases do not have significant health effects and follow-up observation is usually required 4 . While malignant thyroid tumors need timely diagnosis and treatmentumors, otherwise it will have serious consequences for human health. It, mainly manifested in the rapid expansion of the tumor, pressure on the trachea, infiltration of the esophagus and recurrent laryngeal nerve, resulting in breathing difficulties, hoarseness, and obstruction when swallowing food. In severe cases, lung metastasis and bone metastasis may also occur in thyroid malignant tumor. Therefore, early detection and intervention are very important for the treatment and control of thyroid tumors. At present, the early diagnosis of thyroid cancer mainly includes traditional palpation and ultrasound detection. Fine needle aspiration (FNA) is considered as a reliable test for the diagnosis of thyroid cancer 5 . However, there are some objective limitations to the above detection methods, such as requiring an experienced physician to give accurate interpretations and being an invasive test. Misdiagnosis of thyroid cancer comes with some real harms, such as unnecessary surgery and potential complications for patients, resulting in economic burdens for individuals and society, increasing the mental burden of patients, and affecting the quality of life 5 . Consequently, it is meaningful to find new, suitable and accurate biomarkers to distinguish benign and malignant thyroid tumors. As for the risk factors for thyroid tumors, only genetic factors and ionizing radiation are known to play a part in the development of a small number of patients. The cause of most thyroid cancers is unknown. In recent years, several meta-analyses have shown that obesity may be associated with poor prognosis of various cancers. One study found that the aggressiveness of papillary thyroid cancer (PTC) is also potentially affected by obesity 6 . Obesity is closely related to dyslipidemia. Dyslipidemia is one of the considerable risk factors for cancer. Relevant clinical evidence shows that changes in lipid metabolism can lead to the occurrence, invasion and metastasis of cancer through a diversity of signaling pathways. Although there are significant individual differences in blood lipid levels, the vast majority of current studies still focus on the average level of blood lipid or a single value 7 . The close correlation between abnormal lipid metabolism and malignant tumor has been confirmed by many studies 8 , mainly reflected in the occurrence, development, metastasis and prognosis of malignant tumors. For example, in the study of liver cancer, a number of studies have confirmed that high HDL-C level may be a protective factor for liver malignancy. Besides, studies on blood lipids in gastric cancer, breast cancer, colorectal cancer and other tumors have also been reported. However, there are few studies on the correlation between thyroid tumors and lipid levels. Thus, this study aims to analyze the correlation between blood lipid levels and thyroid tumors, and to find potential indicators for the evaluation of thyroid tumors and the mechanism of disease development. In this study, we found that blood lipids were closely related not only to benign and malignant thyroid tumors, but also to malignant thyroid metastasis and tumor diameter, which provided clinical basis for relevant basic research in the future. Materials and methods Study population This study included 915 patients with thyroid tumors from Chongqing General Hospital from July 2020 to September 2021. According to the WHO 2017 Classification and AJCC/TNM Classification 8th Edition (TNM-8), it was divided into two groups: benign and malignant. Meanwhile, the diagnosis of all patients were consistent with pathological findings, and patients with cardiovascular disease were excluded based on medical history. The research methods of this study were conducted in accordance with relevant guidelines and regulations and the study was exempted from ethical review by Chongqing General Hospital. Data collection The diagnosis and treatment records, laboratory test results, doctor's orders and other data were all derived from the electronic medical record (EMR) system of Chongqing General Hospital. According to the purpose of this study, patient’s demographic and lipid indictors of patients were selected. The results of the patients' initial lipid level examination were selected. Demographic indicators included sex, age, body mass index (BMI), and blood lipid indicators included total cholesterol (TCHO), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), lipoprotein (a) (LP(a)), blood lipid ratio AI, THR, LHR. The AI, THR, LHR were calculated as follows (Table 1 ): Table 1 The AI, THR, LHR calculation formula Statistical methods Qualitative or categorical demographic and blood lipid variables were expressed in frequency and proportion. Firstly, the normality of the data was checked using the Shapiro-Wilk test method. The normally distributed data were reported as mean and standard deviation, while the non-normal distribution data were reported as the median quartile interval. Secondly, the comparison between the benign and malignant thyroid tumor groups were evaluated by Mann-Whitney test and Chi-square test. Finally,the relationship between the blood lipid level and thyroid tumors were assessed by Spearman correlation analysis, dose-effect analysis and logistic regression methods( p < 0.05 was considered statistically significant). Results Description of the study population A total of 915 patients with thyroid tumors were selected in this study, including 151 patients with benign thyroid tumors and 764 patients with malignant thyroid tumors. The following table (Table 2 ) shows the characteristics of the study population. In the malignant group, the median age was 39 years and 77.7% were female. The median age in the benign group was 45 years and 84.1% were women. There was a significant difference in age between the benign and malignant groups. BMI is an indictor reflecting the degree of obesity. The BMI in the malignant group was 23.24 [20.96, 25.39], and that in the benign group was 23.23 [21.41, 24.75], and there was no significant difference between the two groups. This suggests that there was no difference in obesity levels between the two groups. Table 2 Characteristics of the study population Indictors Calculation formula AI (TCHO-HDL-C)/HDL-C THR TG/ HDL-C LHR LDL-C/HDL-C Indictors Malignancy (n = 764) Benign(n = 151) p Age (years) 39.00 [33.00, 49.00] 45.00 [35.00, 51.00] 0.011 Sex (female) 594 (77.7) 127 (84.1) 0.082 BMI (kg/m 2 ) 23.24 [20.96, 25.39] 23.23 [21.41, 24.75] 0.714 TCHO (mmol/L) 4.85 (0.98) 5.14 (0.91) 0.001 HDL-C (mmol/L) 1.37 [1.16, 1.59] 1.48 [1.25, 1.66] 0.001 LDL-C (mmol/L) 2.50 [2.05, 2.98] 2.68 [2.20, 3.14] 0.006 TG (mmol/L) 1.16 [0.83, 1.77] 1.19 [0.90, 1.52] 0.915 LP(a) (mmol/L) 111.00 [64.00, 226.25] 133.00 [71.00, 293.00] 0.015 AI 2.42 [1.87, 3.15] 2.45 [2.05, 3.05] 0.779 THR 0.84 [0.54, 1.44] 0.80 [0.58, 1.14] 0.311 LHR 1.82 [1.44, 2.31] 1.82 [1.55, 2.28] 0.788 Analysis of differences in lipid levels between benign and malignant thyroid tumors Mann-Whitney U test and chi-square test were selected to analyze the difference in blood lipid levels between the two groups of thyroid tumor patients. The results are shown in Table 2 . Age, TCHO, HDL-C, LDL-C, LP(a) were significantly different between the benign and malignant groups. Moreover, we conducted correlation analysis of the different lipid levels between the two groups after univariate analysis (Fig. 1 ). The results showed that there was a linear correlation between TCHO and HDL-C, LDL-C, LP(a), and there was a linear correlation between LDL-C and LP(a). The coefficient after correlation coefficient test was statistically significant. In particular, the correlation coefficient between TCHO and LDL-C was 0.856, suggesting that they were highly correlated. Then, we used spline regression model to preliminary explore the shape of association between the lipid levels and benign and malignant thyroid tumors. The restricted cubic spline function uses the default three knots (at the 10th, 50th, 90th percentiles). The adjusted model showed the dose response relationship between the lipid levels and benign and malignant thyroid tumors (Fig. 2 ). The logistic regression analyze was showed in the table blelow (Table 3 ). There was no collinearity between all lipid levels (VIF < 10). According to the P value and OR value of logistic regression analyzes results, we found that the OR value of HDL-C was 0.493(0.272,0.894), and the P value was 0.02. This suggests that HDL-C is a protective factor for benign and malignant thyroid tumors. The higher of the HDL-C level, the more likely the tumor is to be benign, and the lower the level of HDL-C, the more likely the tumor is to be malignant. Except HDL-C, other lipid levels could not be considered as risk factors or protective factors for benign and malignant thyroid tumors ( p > 0.05). Table 3 logistic regression analysis results after single factor analysis Indicators B P OR (95%CI) VIF TCHO -0.184 0.318 0.832(0.579,1.195) 4.249 HDL-C -0.707 0.020 0.493(0.272,0.894) 1.151 LDL-C -0.071 0.786 0.932(0.560,1.551) 4.101 LP(a) 0.000 0.348 1.000(0.999,1.000) 1.080 constant 3.829 0.000 Analysis of the correlation between lipid levels and malignant thyroid tumors In order to further explore the relationship between blood lipid level and thyroid tumor. We analyzed the relationship between lipid levels and BRAF genotype, largest tumor diameter, and lymph node metastasis in patients with thyroid tumors (Table 4 ). We found that there was no significant difference between BRAF genotypes (mutant and wild type) and lipid levels. For lymph node metastasis, we found significant differences in HDL-C, AI, THR, and LHR levels in lymph node metastasis. In addition, we also found that HDL-C was negatively correlated with the maximum tumor diameter, and LHR was positively correlated with the maximum tumor diameter. These results suggest that blood lipid levels may be related to the growth and metastasis of thyroid tumors. Table 4 correlation between lipid levels and malignant thyroid tumors Indictors lymph node metastasis BRAF genotype Largest tumor diameter Z P Z P Pearson's R P TCHO -0.893 0.372 -0.821 0.411 0.005 0.918 HDL-C -2.820 0.005 -0.675 0.500 -0.100 0.024 LDL-C -1.658 0.097 -0.642 0.521 0.041 0.359 TG -1.399 0.162 -0.755 0.450 0.024 0.585 LP(a) -0.338 0.735 -0.272 0.786 0.031 0.491 AI -3.068 0.002 -0.001 0.999 0.074 0.095 THR -1.966 0.049 -0.794 0.427 0.032 0.470 LHR -3.354 0.001 -0.077 0.939 0.088 0.048 Discussion In recent years, the incidence of thyroid cancer has increased in the world, showing an increasing trend year by year 9 . Base on the International Agency for Research on Cancer (IARC), approximately 586,200 people worldwide will be newly diagnosed with thyroid cancer in 2020, ranking thyroid cancer the 9th most common malignancy 10 . Thyroid tumors threaten human health and life, and also cause serious psychological and economic burden to patients. Therefore, it is essential to find a suitable biomarker for distinguish malignant thyroid tumors. With the in-depth study of lipid metabolism, it is found that lipid metabolism not only plays a key role in the occurrence and development of cardiovascular diseases, metabolic syndrome and other chronic diseases 11 , but also has a significant impact on the occurrence and development of malignant tumors 12 . Studies 13 , 14 have found that low level of HDL-C in gastric cancer is inversely proportional to the rate of lymph node metastasis and vascular invasion, while low level of total cholesterol is directly proportional to the mortality of gastric cancer patients. Studies have also shown that the postoperative survival rate of stage II and III colon cancer patients is positively correlated with HDL-C level, but the clear mechanism of its anti-tumor activity is still unclear 15 . In addition, the correlation between lipid level and various malignant tumors such as breast cancer, liver cancer and lung cancer has also been confirmed 16 – 18 . The association between thyroid cancer and blood lipid was first discovered in 1999, and Vitale et al. proved its possible association with statins through in vitro experiments 19 . Later studies have found that, based on HMG-CoA reductase inhibitors inhibiting propyl thiouracil-induced goiter, statins are expected to be a treatment option for benign and malignant proliferative thyroid diseases 20 . A large cohort study conducted in Austria showed that the risk of thyroid cancer rose by about 2 times when TG concentrations were increased, and considered TG concentrations to be a risk factor for thyroid cancer 21 . In addition, studies from Germany have shown that the increase of total cholesterol level is negatively correlated with cancer 22 , and targeted treatment of cholesterol metabolism can become a new strategy for the treatment of thyroid tumors with poor prognosis. In this study, we found that there may be a certain correlation between blood lipid levels and thyroid tumors. Serum levels of TCHO, HDL-C, LDL-C and LP(a) in patients with malignant thyroid tumors were significantly lower than those in patients with benign thyroid tumors. Similar conclusions have also been found in liver cancer, the reason for the reduction of TCHO in liver cancer is believed to be due to the increased consumption of cholesterol by cancer lesions, that is, the reduction of total serum cholesterol content in the human body may be caused by the metabolic consumption of cancer or the nutritional status of patients themselves. With the change of LDL-C and LP(a) in plasma, the content will also decrease significantly 23 . In addition, the logistic regression analysis in this study suggests that HDL-C is a protective factor for benign and malignant thyroid tumors, which is also consistent with the conclusions of existing studies on the relationship between HDL-C and tumors 24 . More importantly, we found significant differences in HDL-C, AI, THR, and LHR levels in lymph node metastasis. In addition, we also found that HDL-C was negatively correlated with the maximum tumor diameter, and LHR was positively correlated with the maximum tumor diameter, suggesting that lipid levels might also be correlated with thyroid tumor growth and metastasis. The limitations of this study are mainly reflected in that the study was conducted in only one unit center and the sample size was not large enough. In addition, due to the fact that patients with benign tumors are usually not hospitalized for treatment, some clinical data are missing and cannot be analyzed, so the proportion of benign patients in the group is relatively small. Although the correlation between lipid level and thyroid tumors was clinically significant, its correlation coefficient was not significant enough. Therefore, the clinical value of lipid levels as an indicator for thyroid tumor evaluation needs to be confirmed in an independent multicenter validation cohort. In summary, lipid level is expected to be an effective indicator for predicting thyroid cancer, but more in-depth studies are needed to confirm it. With the deepening of the research, the specific mechanism of its correlation is expected to be clarified. Declarations Conflict of interest No potential conflict of interest was reported by the authors. Consent for publication Manuscript is approved by all authors for publication. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution SL. Y-yG. and PL. took part in the research design and helped to draft the manuscript. Z-jL. contributed the acquisition of data. JG. performed the statistical analysis. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Jung CK, Bychkov A, Kakudo K. Update from the 2022 World Health Organization Classification of Thyroid Tumors: A Standardized Diagnostic Approach. Endocrinol Metab (Seoul). 2022;37(5):703–18. 10.3803/EnM.2022.1553 . Cheng F, Xiao J, Shao C, et al. Burden of Thyroid Cancer From 1990 to 2019 and Projections of Incidence and Mortality Until 2039 in China: Findings From Global Burden of Disease Study. Front Endocrinol (Lausanne). 2021;12(738213). 10.3389/fendo.2021.738213 . Quality control index for standardized diagnosis and treatment of thyroid cancer in China. (2022 edition). Zhonghua Zhong Liu Za Zhi 2022;44(9):902–907, 10.3760/cma.j.cn112152-20220707-00477 . Grani G, Sponziello M, Pecce V, et al. Contemporary Thyroid Nodule Evaluation and Management. J Clin Endocrinol Metab. 2020;105(9):2869–83. 10.1210/clinem/dgaa322 . Chen DW, Lang BHH, McLeod DSA, et al. Thyroid cancer Lancet. 2023;401(10387):1531–44. 10.1016/s0140-6736(23)00020-x . Liu F, Zhang X. Hypertension and Obesity: Risk Factors for Thyroid Disease. Front Endocrinol (Lausanne). 2022;13(939367). 10.3389/fendo.2022.939367 . Chan JSK, Satti DI, Lee YHA, et al. Association Between Visit-to-Visit Lipid Variability and Incident Cancer: A Population-based Cohort Study. Curr Probl Cardiol. 2023;48(1):101421. 10.1016/j.cpcardiol.2022.101421 . Martin-Perez M, Urdiroz-Urricelqui U, Bigas C, et al. The role of lipids in cancer progression and metastasis. Cell Metab. 2022;34(11):1675–99. 10.1016/j.cmet.2022.09.023 . Li M, Dal Maso L, Vaccarella S. Global trends in thyroid cancer incidence and the impact of overdiagnosis. Lancet Diabetes Endocrinol. 2020;8(6):468–70. 10.1016/s2213-8587(20)30115-7 . Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660 . Hsu JJ, Tintut Y, Demer LL. Regulation of cardiovascular calcification by lipids and lipoproteins. Curr Opin Lipidol. 2022;33(5):289–94. 10.1097/mol.0000000000000844 . Butler LM, Perone Y, Dehairs J, et al. Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention. Adv Drug Deliv Rev. 2020;159:245–93. 10.1016/j.addr.2020.07.013 . Lim SC, Parajuli KR, Duong HQ, et al. Cholesterol induces autophagic and apoptotic death in gastric carcinoma cells. Int J Oncol. 2014;44(3):805–11. 10.3892/ijo.2014.2246 . Tamura T, Inagawa S, Hisakura K, et al. Evaluation of serum high-density lipoprotein cholesterol levels as a prognostic factor in gastric cancer patients. J Gastroenterol Hepatol. 2012;27(10):1635–40. 10.1111/j.1440-1746.2012.07189.x . Wang Y, Sun XQ, Lin HC, et al. Correlation between immune signature and high-density lipoprotein cholesterol level in stage II/III colorectal cancer. Cancer Med. 2019;8(3):1209–17. 10.1002/cam4.1987 . Altundag K. Association between preoperative serum vitamin D and high-density lipoprotein-cholesterol levels and effects on breast cancer prognosis. Breast. 2018;37(215). 10.1016/j.breast.2016.12.009 . Karayama M, Inui N, Inoue Y, et al. Increased serum cholesterol and long-chain fatty acid levels are associated with the efficacy of nivolumab in patients with non-small cell lung cancer. Cancer Immunol Immunother. 2022;71(1):203–17. 10.1007/s00262-021-02979-4 . Cui YN, Gao Q, Zhu SS, et al. Effect of serum lipid level on prognosis of patients with small cell lung cancer at the initial treatment. Zhonghua Zhong Liu Za Zhi. 2021;43(3):318–23. 10.3760/cma.j.cn112152-20190813-00517 . Robbins RJ. Statins sentence thyroid cancer cells to death rho. J Clin Endocrinol Metab. 2003;88(7):3019–20. 10.1210/jc.2003-030827 . Trivedi LU, Femnou Mbuntum L, Halm EA, et al. Is Statin Use Associated With Risk of Thyroid Diseases? Results of a Retrospective Cohort Study. Ann Pharmacother. 2021;55(9):1110–9. 10.1177/1060028020986552 . Ulmer H, Borena W, Rapp K, et al. Serum triglyceride concentrations and cancer risk in a large cohort study in Austria. Br J Cancer. 2009;101(7):1202–6. 10.1038/sj.bjc.6605264 . Loosen SH, Kostev K, Luedde M, et al. Low blood levels of high-density lipoprotein (HDL) cholesterol are positively associated with cancer. J Cancer Res Clin Oncol. 2022;148(11):3039–46. 10.1007/s00432-021-03867-1 . Jiang J, Nilsson-Ehle P, Xu N. Influence of liver cancer on lipid and lipoprotein metabolism. Lipids Health Dis. 2006;5(4). 10.1186/1476-511x-5-4 . Soran H, Schofield JD, Durrington PN. Antioxidant properties of HDL. Front Pharmacol. 2015;6(222). 10.3389/fphar.2015.00222 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4108925","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284337346,"identity":"1599ba14-0903-49f7-a473-3a13a5be1040","order_by":0,"name":"Shuang Liu","email":"","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Liu","suffix":""},{"id":284337347,"identity":"226045bd-e1f3-451b-b49f-fdb1166bd95a","order_by":1,"name":"Yuanyuan Guo","email":"","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Guo","suffix":""},{"id":284337348,"identity":"4fe17bc2-aa0b-40db-8d4c-08beeeb62127","order_by":2,"name":"Zhijie Li","email":"","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhijie","middleName":"","lastName":"Li","suffix":""},{"id":284337349,"identity":"5d8a0eb7-25df-45b7-90de-4308665da0cb","order_by":3,"name":"Jun Gong","email":"","orcid":"","institution":"University-Town Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Gong","suffix":""},{"id":284337350,"identity":"760fd559-c74a-4054-9304-d44b82c5b04b","order_by":4,"name":"Pu Liao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACPmYwZUOCFjaIljRStECow6RoYWd++Liw7Xxif/vxBww/ahjkzQk7jM3YeGbb7cQZZ3IMGHuOMRjubCCohcFMmheoZQNDDgMDbwNDgsEBglrYvwG1nEvcwP/8AeNf4rTwgGw5kLhBIsGAmUhbeIqNZ5xLNp5x443BYZljEoYbCGnh5z++8XFBmZ1sf3/6w4dvamzkCdoCAswwBlCxBBHqkbWMglEwCkbBKMAKAGfgN3PU5nyCAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Pu","middleName":"","lastName":"Liao","suffix":""}],"badges":[],"createdAt":"2024-03-15 15:18:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4108925/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4108925/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53752623,"identity":"a1360528-8390-4ab2-89b5-2d6d97f0cd81","added_by":"auto","created_at":"2024-03-29 18:51:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58563,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between blood lipid level and thyroid tumors.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4108925/v1/40f241f65b2a92860870a63e.jpg"},{"id":53752621,"identity":"fe9f0fab-8108-4e6f-8e7b-a8afebcf16b7","added_by":"auto","created_at":"2024-03-29 18:51:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29361,"visible":true,"origin":"","legend":"\u003cp\u003eDose-effect between blood lipid level and thyroid tumors\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4108925/v1/5a98a4a46537f592740baa48.jpg"},{"id":68224103,"identity":"0b7f8003-d767-4bbf-ae29-a69c2d7895e8","added_by":"auto","created_at":"2024-11-05 02:46:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":551048,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4108925/v1/9a07c350-4fc6-4d91-b5d4-81fe7158e4cb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Analysis of Blood Lipid Level and Thyroid Tumors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid cancer is the most common malignant tumor of the head and neck, and it originates from the follicular epithelium or parafollicular epithelial cells of the thyroid gland\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In 2019, there were 233,846 new cases of thyroid cancer worldwide and 45,575 deaths from thyroid cancer, of which the proportion in China was 16.71% and 15.88% respectively. China has the highest number of thyroid cancer cases and deaths in the world, and the incidence has risen sharply in some provinces\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. According to the survey of the Chinese Tumor Registry Center, the incidence of thyroid cancer in urban Chinese women ranks fourth among female malignant tumors and will continue to increase by 20% per year\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. For benign thyroid tumors, most cases do not have significant health effects and follow-up observation is usually required\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. While malignant thyroid tumors need timely diagnosis and treatmentumors, otherwise it will have serious consequences for human health. It, mainly manifested in the rapid expansion of the tumor, pressure on the trachea, infiltration of the esophagus and recurrent laryngeal nerve, resulting in breathing difficulties, hoarseness, and obstruction when swallowing food. In severe cases, lung metastasis and bone metastasis may also occur in thyroid malignant tumor. Therefore, early detection and intervention are very important for the treatment and control of thyroid tumors.\u003c/p\u003e \u003cp\u003eAt present, the early diagnosis of thyroid cancer mainly includes traditional palpation and ultrasound detection. Fine needle aspiration (FNA) is considered as a reliable test for the diagnosis of thyroid cancer\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, there are some objective limitations to the above detection methods, such as requiring an experienced physician to give accurate interpretations and being an invasive test. Misdiagnosis of thyroid cancer comes with some real harms, such as unnecessary surgery and potential complications for patients, resulting in economic burdens for individuals and society, increasing the mental burden of patients, and affecting the quality of life\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Consequently, it is meaningful to find new, suitable and accurate biomarkers to distinguish benign and malignant thyroid tumors.\u003c/p\u003e \u003cp\u003eAs for the risk factors for thyroid tumors, only genetic factors and ionizing radiation are known to play a part in the development of a small number of patients. The cause of most thyroid cancers is unknown. In recent years, several meta-analyses have shown that obesity may be associated with poor prognosis of various cancers. One study found that the aggressiveness of papillary thyroid cancer (PTC) is also potentially affected by obesity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Obesity is closely related to dyslipidemia. Dyslipidemia is one of the considerable risk factors for cancer. Relevant clinical evidence shows that changes in lipid metabolism can lead to the occurrence, invasion and metastasis of cancer through a diversity of signaling pathways. Although there are significant individual differences in blood lipid levels, the vast majority of current studies still focus on the average level of blood lipid or a single value\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The close correlation between abnormal lipid metabolism and malignant tumor has been confirmed by many studies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, mainly reflected in the occurrence, development, metastasis and prognosis of malignant tumors. For example, in the study of liver cancer, a number of studies have confirmed that high HDL-C level may be a protective factor for liver malignancy. Besides, studies on blood lipids in gastric cancer, breast cancer, colorectal cancer and other tumors have also been reported. However, there are few studies on the correlation between thyroid tumors and lipid levels.\u003c/p\u003e \u003cp\u003eThus, this study aims to analyze the correlation between blood lipid levels and thyroid tumors, and to find potential indicators for the evaluation of thyroid tumors and the mechanism of disease development. In this study, we found that blood lipids were closely related not only to benign and malignant thyroid tumors, but also to malignant thyroid metastasis and tumor diameter, which provided clinical basis for relevant basic research in the future.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study included 915 patients with thyroid tumors from Chongqing General Hospital from July 2020 to September 2021. According to the WHO 2017 Classification and AJCC/TNM Classification 8th Edition (TNM-8), it was divided into two groups: benign and malignant. Meanwhile, the diagnosis of all patients were consistent with pathological findings, and patients with cardiovascular disease were excluded based on medical history. The research methods of this study were conducted in accordance with relevant guidelines and regulations and the study was exempted from ethical review by Chongqing General Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe diagnosis and treatment records, laboratory test results, doctor's orders and other data were all derived from the electronic medical record (EMR) system of Chongqing General Hospital. According to the purpose of this study, patient\u0026rsquo;s demographic and lipid indictors of patients were selected. The results of the patients' initial lipid level examination were selected.\u003c/p\u003e \u003cp\u003eDemographic indicators included sex, age, body mass index (BMI), and blood lipid indicators included total cholesterol (TCHO), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), lipoprotein (a) (LP(a)), blood lipid ratio AI, THR, LHR. The AI, THR, LHR were calculated as follows (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe AI, THR, LHR calculation formula\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eQualitative or categorical demographic and blood lipid variables were expressed in frequency and proportion. Firstly, the normality of the data was checked using the Shapiro-Wilk test method. The normally distributed data were reported as mean and standard deviation, while the non-normal distribution data were reported as the median quartile interval. Secondly, the comparison between the benign and malignant thyroid tumor groups were evaluated by Mann-Whitney test and Chi-square test. Finally,the relationship between the blood lipid level and thyroid tumors were assessed by Spearman correlation analysis, dose-effect analysis and logistic regression methods(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the study population\u003c/h2\u003e \u003cp\u003eA total of 915 patients with thyroid tumors were selected in this study, including 151 patients with benign thyroid tumors and 764 patients with malignant thyroid tumors. The following table (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows the characteristics of the study population. In the malignant group, the median age was 39 years and 77.7% were female. The median age in the benign group was 45 years and 84.1% were women. There was a significant difference in age between the benign and malignant groups. BMI is an indictor reflecting the degree of obesity. The BMI in the malignant group was 23.24 [20.96, 25.39], and that in the benign group was 23.23 [21.41, 24.75], and there was no significant difference between the two groups. This suggests that there was no difference in obesity levels between the two 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\u003eCharacteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalculation formula\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(TCHO-HDL-C)/HDL-C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG/ HDL-C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLDL-C/HDL-C\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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\u003eIndictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignancy (n\u0026thinsp;=\u0026thinsp;764)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign(n\u0026thinsp;=\u0026thinsp;151)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.00 [33.00, 49.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.00 [35.00, 51.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e594 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127 (84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\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)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.24 [20.96, 25.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.23 [21.41, 24.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCHO (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.85 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.14 (0.91)\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\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37 [1.16, 1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48 [1.25, 1.66]\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)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.50 [2.05, 2.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68 [2.20, 3.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\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\u003e1.16 [0.83, 1.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19 [0.90, 1.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLP(a) (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111.00 [64.00, 226.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133.00 [71.00, 293.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.42 [1.87, 3.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.45 [2.05, 3.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84 [0.54, 1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80 [0.58, 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.82 [1.44, 2.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82 [1.55, 2.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of differences in lipid levels between benign and malignant thyroid tumors\u003c/h2\u003e \u003cp\u003eMann-Whitney U test and chi-square test were selected to analyze the difference in blood lipid levels between the two groups of thyroid tumor patients. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Age, TCHO, HDL-C, LDL-C, LP(a) were significantly different between the benign and malignant groups.\u003c/p\u003e \u003cp\u003eMoreover, we conducted correlation analysis of the different lipid levels between the two groups after univariate analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results showed that there was a linear correlation between TCHO and HDL-C, LDL-C, LP(a), and there was a linear correlation between LDL-C and LP(a). The coefficient after correlation coefficient test was statistically significant. In particular, the correlation coefficient between TCHO and LDL-C was 0.856, suggesting that they were highly correlated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThen, we used spline regression model to preliminary explore the shape of association between the lipid levels and benign and malignant thyroid tumors. The restricted cubic spline function uses the default three knots (at the 10th, 50th, 90th percentiles). The adjusted model showed the dose response relationship between the lipid levels and benign and malignant thyroid tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe logistic regression analyze was showed in the table blelow (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There was no collinearity between all lipid levels (VIF\u0026thinsp;\u0026lt;\u0026thinsp;10). According to the P value and OR value of logistic regression analyzes results, we found that the OR value of HDL-C was 0.493(0.272,0.894), and the P value was 0.02. This suggests that HDL-C is a protective factor for benign and malignant thyroid tumors. The higher of the HDL-C level, the more likely the tumor is to be benign, and the lower the level of HDL-C, the more likely the tumor is to be malignant. Except HDL-C, other lipid levels could not be considered as risk factors or protective factors for benign and malignant thyroid tumors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003elogistic regression analysis results after single factor analysis\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.832(0.579,1.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.249\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-0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.493(0.272,0.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.151\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\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.932(0.560,1.551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLP(a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000(0.999,1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the correlation between lipid levels and malignant thyroid tumors\u003c/h2\u003e \u003cp\u003eIn order to further explore the relationship between blood lipid level and thyroid tumor. We analyzed the relationship between lipid levels and BRAF genotype, largest tumor diameter, and lymph node metastasis in patients with thyroid tumors (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We found that there was no significant difference between BRAF genotypes (mutant and wild type) and lipid levels. For lymph node metastasis, we found significant differences in HDL-C, AI, THR, and LHR levels in lymph node metastasis. In addition, we also found that HDL-C was negatively correlated with the maximum tumor diameter, and LHR was positively correlated with the maximum tumor diameter. These results suggest that blood lipid levels may be related to the growth and metastasis of thyroid tumors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ecorrelation between lipid levels and malignant thyroid tumors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elymph node metastasis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBRAF genotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLargest tumor diameter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePearson's R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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\u003eTCHO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLP(a)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, the incidence of thyroid cancer has increased in the world, showing an increasing trend year by year\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Base on the International Agency for Research on Cancer (IARC), approximately 586,200 people worldwide will be newly diagnosed with thyroid cancer in 2020, ranking thyroid cancer the 9th most common malignancy\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Thyroid tumors threaten human health and life, and also cause serious psychological and economic burden to patients. Therefore, it is essential to find a suitable biomarker for distinguish malignant thyroid tumors.\u003c/p\u003e \u003cp\u003eWith the in-depth study of lipid metabolism, it is found that lipid metabolism not only plays a key role in the occurrence and development of cardiovascular diseases, metabolic syndrome and other chronic diseases\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, but also has a significant impact on the occurrence and development of malignant tumors\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Studies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e have found that low level of HDL-C in gastric cancer is inversely proportional to the rate of lymph node metastasis and vascular invasion, while low level of total cholesterol is directly proportional to the mortality of gastric cancer patients. Studies have also shown that the postoperative survival rate of stage II and III colon cancer patients is positively correlated with HDL-C level, but the clear mechanism of its anti-tumor activity is still unclear\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In addition, the correlation between lipid level and various malignant tumors such as breast cancer, liver cancer and lung cancer has also been confirmed\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe association between thyroid cancer and blood lipid was first discovered in 1999, and Vitale et al. proved its possible association with statins through in vitro experiments\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Later studies have found that, based on HMG-CoA reductase inhibitors inhibiting propyl thiouracil-induced goiter, statins are expected to be a treatment option for benign and malignant proliferative thyroid diseases\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. A large cohort study conducted in Austria showed that the risk of thyroid cancer rose by about 2 times when TG concentrations were increased, and considered TG concentrations to be a risk factor for thyroid cancer\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition, studies from Germany have shown that the increase of total cholesterol level is negatively correlated with cancer\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and targeted treatment of cholesterol metabolism can become a new strategy for the treatment of thyroid tumors with poor prognosis.\u003c/p\u003e \u003cp\u003eIn this study, we found that there may be a certain correlation between blood lipid levels and thyroid tumors. Serum levels of TCHO, HDL-C, LDL-C and LP(a) in patients with malignant thyroid tumors were significantly lower than those in patients with benign thyroid tumors. Similar conclusions have also been found in liver cancer, the reason for the reduction of TCHO in liver cancer is believed to be due to the increased consumption of cholesterol by cancer lesions, that is, the reduction of total serum cholesterol content in the human body may be caused by the metabolic consumption of cancer or the nutritional status of patients themselves. With the change of LDL-C and LP(a) in plasma, the content will also decrease significantly\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In addition, the logistic regression analysis in this study suggests that HDL-C is a protective factor for benign and malignant thyroid tumors, which is also consistent with the conclusions of existing studies on the relationship between HDL-C and tumors\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. More importantly, we found significant differences in HDL-C, AI, THR, and LHR levels in lymph node metastasis. In addition, we also found that HDL-C was negatively correlated with the maximum tumor diameter, and LHR was positively correlated with the maximum tumor diameter, suggesting that lipid levels might also be correlated with thyroid tumor growth and metastasis.\u003c/p\u003e \u003cp\u003eThe limitations of this study are mainly reflected in that the study was conducted in only one unit center and the sample size was not large enough. In addition, due to the fact that patients with benign tumors are usually not hospitalized for treatment, some clinical data are missing and cannot be analyzed, so the proportion of benign patients in the group is relatively small. Although the correlation between lipid level and thyroid tumors was clinically significant, its correlation coefficient was not significant enough. Therefore, the clinical value of lipid levels as an indicator for thyroid tumor evaluation needs to be confirmed in an independent multicenter validation cohort.\u003c/p\u003e \u003cp\u003eIn summary, lipid level is expected to be an effective indicator for predicting thyroid cancer, but more in-depth studies are needed to confirm it. With the deepening of the research, the specific mechanism of its correlation is expected to be clarified.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003e Manuscript is approved by all authors for publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSL. Y-yG. and PL. took part in the research design and helped to draft the manuscript. Z-jL. contributed the acquisition of data. JG. performed the statistical analysis. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJung CK, Bychkov A, Kakudo K. Update from the 2022 World Health Organization Classification of Thyroid Tumors: A Standardized Diagnostic Approach. Endocrinol Metab (Seoul). 2022;37(5):703\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3803/EnM.2022.1553\u003c/span\u003e\u003cspan address=\"10.3803/EnM.2022.1553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng F, Xiao J, Shao C, et al. Burden of Thyroid Cancer From 1990 to 2019 and Projections of Incidence and Mortality Until 2039 in China: Findings From Global Burden of Disease Study. Front Endocrinol (Lausanne). 2021;12(738213). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2021.738213\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2021.738213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuality control index for standardized diagnosis and treatment of thyroid cancer in China. (2022 edition). Zhonghua Zhong Liu Za Zhi 2022;44(9):902\u0026ndash;907, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3760/cma.j.cn112152-20220707-00477\u003c/span\u003e\u003cspan address=\"10.3760/cma.j.cn112152-20220707-00477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrani G, Sponziello M, Pecce V, et al. Contemporary Thyroid Nodule Evaluation and Management. J Clin Endocrinol Metab. 2020;105(9):2869\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/clinem/dgaa322\u003c/span\u003e\u003cspan address=\"10.1210/clinem/dgaa322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen DW, Lang BHH, McLeod DSA, et al. Thyroid cancer Lancet. 2023;401(10387):1531\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(23)00020-x\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(23)00020-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu F, Zhang X. Hypertension and Obesity: Risk Factors for Thyroid Disease. Front Endocrinol (Lausanne). 2022;13(939367). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2022.939367\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2022.939367\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan JSK, Satti DI, Lee YHA, et al. Association Between Visit-to-Visit Lipid Variability and Incident Cancer: A Population-based Cohort Study. Curr Probl Cardiol. 2023;48(1):101421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cpcardiol.2022.101421\u003c/span\u003e\u003cspan address=\"10.1016/j.cpcardiol.2022.101421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin-Perez M, Urdiroz-Urricelqui U, Bigas C, et al. The role of lipids in cancer progression and metastasis. Cell Metab. 2022;34(11):1675\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2022.09.023\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2022.09.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Dal Maso L, Vaccarella S. Global trends in thyroid cancer incidence and the impact of overdiagnosis. Lancet Diabetes Endocrinol. 2020;8(6):468\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2213-8587(20)30115-7\u003c/span\u003e\u003cspan address=\"10.1016/s2213-8587(20)30115-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21660\u003c/span\u003e\u003cspan address=\"10.3322/caac.21660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu JJ, Tintut Y, Demer LL. Regulation of cardiovascular calcification by lipids and lipoproteins. Curr Opin Lipidol. 2022;33(5):289\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/mol.0000000000000844\u003c/span\u003e\u003cspan address=\"10.1097/mol.0000000000000844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButler LM, Perone Y, Dehairs J, et al. Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention. Adv Drug Deliv Rev. 2020;159:245\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.addr.2020.07.013\u003c/span\u003e\u003cspan address=\"10.1016/j.addr.2020.07.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim SC, Parajuli KR, Duong HQ, et al. Cholesterol induces autophagic and apoptotic death in gastric carcinoma cells. Int J Oncol. 2014;44(3):805\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ijo.2014.2246\u003c/span\u003e\u003cspan address=\"10.3892/ijo.2014.2246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamura T, Inagawa S, Hisakura K, et al. Evaluation of serum high-density lipoprotein cholesterol levels as a prognostic factor in gastric cancer patients. J Gastroenterol Hepatol. 2012;27(10):1635\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1440-1746.2012.07189.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1440-1746.2012.07189.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Sun XQ, Lin HC, et al. Correlation between immune signature and high-density lipoprotein cholesterol level in stage II/III colorectal cancer. Cancer Med. 2019;8(3):1209\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.1987\u003c/span\u003e\u003cspan address=\"10.1002/cam4.1987\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltundag K. Association between preoperative serum vitamin D and high-density lipoprotein-cholesterol levels and effects on breast cancer prognosis. Breast. 2018;37(215). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.breast.2016.12.009\u003c/span\u003e\u003cspan address=\"10.1016/j.breast.2016.12.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarayama M, Inui N, Inoue Y, et al. Increased serum cholesterol and long-chain fatty acid levels are associated with the efficacy of nivolumab in patients with non-small cell lung cancer. Cancer Immunol Immunother. 2022;71(1):203\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00262-021-02979-4\u003c/span\u003e\u003cspan address=\"10.1007/s00262-021-02979-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui YN, Gao Q, Zhu SS, et al. Effect of serum lipid level on prognosis of patients with small cell lung cancer at the initial treatment. Zhonghua Zhong Liu Za Zhi. 2021;43(3):318\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3760/cma.j.cn112152-20190813-00517\u003c/span\u003e\u003cspan address=\"10.3760/cma.j.cn112152-20190813-00517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobbins RJ. Statins sentence thyroid cancer cells to death rho. J Clin Endocrinol Metab. 2003;88(7):3019\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2003-030827\u003c/span\u003e\u003cspan address=\"10.1210/jc.2003-030827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrivedi LU, Femnou Mbuntum L, Halm EA, et al. Is Statin Use Associated With Risk of Thyroid Diseases? Results of a Retrospective Cohort Study. Ann Pharmacother. 2021;55(9):1110\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1060028020986552\u003c/span\u003e\u003cspan address=\"10.1177/1060028020986552\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlmer H, Borena W, Rapp K, et al. Serum triglyceride concentrations and cancer risk in a large cohort study in Austria. Br J Cancer. 2009;101(7):1202\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/sj.bjc.6605264\u003c/span\u003e\u003cspan address=\"10.1038/sj.bjc.6605264\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoosen SH, Kostev K, Luedde M, et al. Low blood levels of high-density lipoprotein (HDL) cholesterol are positively associated with cancer. J Cancer Res Clin Oncol. 2022;148(11):3039\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00432-021-03867-1\u003c/span\u003e\u003cspan address=\"10.1007/s00432-021-03867-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang J, Nilsson-Ehle P, Xu N. Influence of liver cancer on lipid and lipoprotein metabolism. Lipids Health Dis. 2006;5(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1476-511x-5-4\u003c/span\u003e\u003cspan address=\"10.1186/1476-511x-5-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoran H, Schofield JD, Durrington PN. Antioxidant properties of HDL. Front Pharmacol. 2015;6(222). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphar.2015.00222\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2015.00222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"lipids, thyroid, tumor","lastPublishedDoi":"10.21203/rs.3.rs-4108925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4108925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe level of blood lipids has been found to reflect the occurrence and development of tumors to a certain extent. This article explores the correlation between blood lipid levels and thyroid tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 915 patients with thyroid tumors were enrolled in this study and divided into two groups according to the benign and malignant tumors\u003cstrong\u003e.\u003c/strong\u003e The total cholesterol (TCHO), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), lipoprotein (a) and other detection indicators before starting treatment were recorded. The comparison between the two groups were evaluated by Mann-Whitney test and Chi-square test. The relationship between the blood lipid level and thyroid tumors were assessed by Spearman correlation analysis , dose-effect analysis and logistic regression methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSerum TCHO (median 4.85 vs 5,14, \u003cem\u003ep\u003c/em\u003e=0.001), HDL-C (median 1.37 vs 1.48, \u003cem\u003ep\u003c/em\u003e=0.001), LDL-C (median 2.50 vs 2.68, \u003cem\u003ep\u003c/em\u003e=0.006) and lipoprotein (a) (median 111 vs 133, \u003cem\u003ep\u003c/em\u003e=0.023) levels of patients with malignant thyroid tumors were significantly lower than those of patients with benign thyroid tumors. The logistic regression models showed that the OR value of HDL-C was 0.493(0.272,0.894), and the \u003cem\u003eP\u003c/em\u003e value was 0.02. This suggests that HDL-C is a protective factor. In addition, HDL-C was negatively correlated with lymph node metastasis(\u003cem\u003ep\u003c/em\u003e=0.005) and the maximum tumor diameter(\u003cem\u003ep\u003c/em\u003e=0.024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eSerum lipid level is correlated with the occurrence and development of thyroid tumors to a certain extent, and the level of HDL-C is a protective factor for thyroid tumors.\u003c/p\u003e","manuscriptTitle":"Correlation Analysis of Blood Lipid Level and Thyroid Tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 18:51:33","doi":"10.21203/rs.3.rs-4108925/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":"824cc954-aca9-4868-a181-85abe9f4d8f7","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-05T02:38:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-29 18:51:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4108925","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4108925","identity":"rs-4108925","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}