Association fasting blood sugar to high-density lipoprotein cholesterol ratio and hypertension: Analysis from the National Health and Nutrition Examination Survey (2003– 2018)

Association fasting blood sugar to high-density lipoprotein cholesterol ratio and hypertension: Analysis from the National Health and Nutrition Examination Survey (2003– 2018) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association fasting blood sugar to high-density lipoprotein cholesterol ratio and hypertension: Analysis from the National Health and Nutrition Examination Survey (2003– 2018) Shengping Luo, Jiayu Zhu, Le Yang, Jing Liu, Yihui Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6213126/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 Hypertension is a chronic disease that poses a significant threat to human health worldwide. The ratio of fasting blood sugar to high-density lipoprotein cholesterol (GHR) is an emerging biomarker associated with various diseases. However, the relationship between GHR and hypertension was still unclear. This study aimed to explore the correlation between GHR and high blood pressure. The study conducted a comprehensive cross-sectional analysis based on data from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018. The study used a multivariate logistic regression model to evaluate the relationship between GHR and hypertension. It also verified the robustness of the results through subgroup analysis. In addition, the study used restricted cubic splines (RCS) to analyze the possible nonlinear relationship between GHR and hypertension, as well as to explore potential threshold effects. Finally, the study evaluated the predictive efficiency of GHR for hypertension through receiver operating characteristic (ROC) curve analysis. The results showed that, without adjusting for confounding factors, GHR was positively correlated with hypertension (OR = 1.20, 95%CI = 1.17–1.22, P < 0.001). After fully adjusting for confounding factors (including gender, race, age, body mass index, smoking status, alcohol consumption, diabetes, stroke, total cholesterol, and creatinine), the positive correlation between GHR and hypertension still existed (OR = 1.06, 95%CI = 1.03–1.09, P < 0.001). Subgroup analysis further showed that the association between GHR and hypertension remained consistent across different subgroups. In addition, ROC analysis revealed a nonlinear relationship between GHR and hypertension, with a turning point of 2.36. Furthermore, ROC analysis indicated that GHR demonstrated high predictive efficiency in univariate and multivariate-adjusted models. A positive correlation was demonstrated between GHR and hypertension. GHR had the potential to serve as a biological marker for hypertension, facilitating its prevention and diagnosis. Health sciences/Biomarkers/Predictive markers Health sciences/Medical research/Biomarkers/Predictive markers Fasting blood sugar to high-density lipoprotein cholesterol ratio Hypertension Cross-sectional study NHANES risk Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Hypertension is a common chronic disease worldwide. It is a significant risk factor for cardiovascular disease and represents a serious threat to public health. Hypertension has emerged as a significant global public health concern, impacting over a billion individuals worldwide 1 . The persistent nature of hypertension exerts a deleterious effect on vital organs, including the heart, brain, and kidneys, leading to severe conditions such as chronic heart failure, cerebral hemorrhage, and Chronic renal failure 2 , 3 .Research findings suggest that in 2019, hypertension was responsible for 10 million deaths 4 , a statistic that serves as a clarion call for heightened global health awareness. Moreover, the economic burden of hypertension is substantial; in the United States, the annual cost of dealing with hypertension and its related diseases is as high as $ 131 billion 5 , which poses a severe challenge to the socio-economic system.With the acceleration of global population aging and the continuous rise in obesity rates, the incidence of hypertension and related diseases is also on the rise. In the future, the prevention and control of hypertension will face even greater tasks and challenges. Regular monitoring is a simple and effective evaluation method for patients diagnosed with hypertension. However, there is currently a lack of practical assessment tools for individuals who are potentially at risk. Therefore, it is imperative to identify predictive factors to facilitate early screening and risk stratification in the management of hypertension. Recognizing these factors enables earlier intervention and preventive measures, thereby reducing the incidence of hypertension and its complications. Hypertension has been demonstrated to be closely linked to blood glucose levels 6 . Hyperglycemic conditions have been shown to disrupt the balance between oxidants and antioxidants within cells, leading to increased oxidative stress 7 , which plays a central role in the pathogenesis of hypertension 8 . Studies show that increased levels of reactive oxygen species (ROS) lead to heightened oxidative stress, which causes endothelial injury, vascular dysfunction, systemic inflammation, and accelerates the process of arteriosclerosis, ultimately triggering the onset of hypertension 9 . Fasting blood sugar, a measure of blood glucose levels, is a reliable indicator. However, its stability is relatively poor and can be influenced by certain foods and medications, such as okra and vitamin D 10 , 11 . High-density lipoprotein (HDL) is a protective lipoprotein that plays a positive role in resisting inflammation and oxidative stress 12 . However, various factors have been demonstrated to influence HDL levels, including gender, alcohol consumption, smoking, and eating habits 13 , 14 . The ratio of fasting blood glucose to high-density lipoprotein (GHR) has been identified as a comprehensive indicator for evaluating the dynamic changes of information by combining fasting blood glucose and high-density lipoprotein levels. Previous studies have found an association between GHR and diseases such as gallstones, acute coronary syndrome, and non-alcoholic fatty liver 15 , 16 , 17 . However, the relationship between GHR and hypertension remains to be thoroughly studied. This study is based on data from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018 and systematically explores the relationship between GHR and hypertension, with particular attention to the impact of continuous changes in GHR on the risk of hypertension. This study aims to provide a more accurate and convenient method for assessing an individual's risk of hypertension. In addition, the research results will provide a scientific basis for healthcare professionals to develop effective prevention and treatment strategies, thereby optimizing the management of hypertension. 2 Materials and methods 2.1 Survey data The National Health and Nutrition Examination Survey (NHANES) is a health and nutrition survey conducted by the National Center for Health Statistics (NCHS) under the Centers for Disease Control and Prevention (CDC) of the United States. The survey is conducted every two years using a complex, stratified, multi-stage probability sampling method. It covers the non-institutionalized civilian population in all 50 states and the District of Columbia. The data from NHANES are made available to the public and encompass demographics, dietary habits, physical examinations, laboratory tests, and other relevant metrics. This data aims to comprehensively assess the health and nutritional status of adults and children in the United States. 2.2 Study population This study summarized data from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018, including 80,312 participants who underwent demographic assessments and laboratory tests and completed health status questionnaires. During the data collation process, some participants were excluded due to incomplete records, including participants who lacked information related to hypertension (n = 30,331), participants who lacked fasting blood glucose or high-density lipoprotein levels (n = 28,747), and other participants who had invalid data (n = 4,899). The final research cohort comprised a total of 17,109 participants. The detailed exclusion process is illustrated in Fig. 1 . 2.3 Evaluation of GHR The biochemical data collected from the participants in this study were sourced from the NHANES laboratory data section. The GHR index was used as the exposure variable, with the calculation formula being fasting blood glucose (mmol/L)/high-density lipoprotein cholesterol level (mmol/L). 2.4 Definition of hypertension According to the “Guidelines for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults” published by the American College of Cardiology/American Heart Association (ACC/AHA) in 2017, hypertension was defined as a systolic blood pressure of ≥ 140 mmHg and/or a diastolic blood pressure of ≥ 90 mmHg. Blood pressure measurements were conducted by professionals following standardized procedures. After resting for 5 minutes, the subjects' blood pressure was measured three times using a mercury sphygmomanometer. Based on these three measurements, individuals with an average systolic blood pressure of ≥ 140 mmHg and/or an average diastolic blood pressure of ≥ 90 mmHg were classified as having hypertension. Additionally, even if the average systolic blood pressure was < 140 mmHg and diastolic blood pressure was < 90 mmHg, individuals who met the criteria of “doctor-diagnosed hypertension” or “were taking antihypertensive medications” were also classified as having hypertension. This method was consistent with the standards used in previous studies to identify individuals with hypertension in the NHANES. 2.5 Covariates In order to investigate the independent correlation between GHR and hypertension, this study screened potential covariates related to hypertension based on previous research, including gender, race, age, body mass index (BMI), smoking status, alcohol consumption, diabetes, stroke, total cholesterol and creatinine 18 , 19 . Race was further subdivided into Mexican-Americans, other Hispanics, non-Hispanic whites, non-Hispanic blacks, and other races. In the context of lifestyle variables, smoking status was defined as having smoked at least 100 cigarettes in their lifetime. In contrast, alcohol consumption was defined as having consumed at least 12 drinks in the past year. Detailed information regarding the data acquisition process could be accessed at www.cdc.gov/nchs/nhanes/ . 2.5 Statistical analysis To ensure the accuracy and reliability of the data, NHANES employed a stratified, multi-stage probability sampling design, combined with appropriate weighting methods. In this study, all statistical analyses utilized sampling weights to reflect the national representativeness of the research subjects. Continuous variables were presented as mean ± standard deviation, and categorical variables were expressed as percentages. A multivariate logistic regression model was employed to examine the association between the growth hormone receptor (GHR) and hypertension. Three models were constructed: the crude model, which included only GHR; model 1, which further adjusted for gender, age, race, and body mass index (BMI); and model 2, which was based on model 1 and additionally included smoking status, alcohol consumption, diabetes, stroke, total cholesterol, urine protein, and creatinine for comprehensive adjustment. Furthermore, the study investigated the potential impact of gender, race, age, BMI, diabetes, and stroke on the relationship between GHR and hypertension through subgroup analysis. Interaction terms were incorporated to assess heterogeneity among subgroups, and the results were presented as a forest plot. Additionally, the Restricted Cubic Spline (RCS) analysis method was employed to explore a possible nonlinear relationship between GHR and hypertension. In the event that a nonlinear relationship was identified, the inflection point was further precisely identified, and the threshold effect was evaluated using the likelihood ratio test. Concurrently, the diagnostic accuracy of GHR in the crude model and model 2 was assessed using Receiver Operating Characteristic (ROC) analysis. All statistical analyses were completed using R software (version 4.4.2), and P < 0.05 was considered statistically significant. 3 Results 3.1 Baseline characteristics of participants This study included 17,109 participants, with 8,410 men and 8,699 women. The prevalence of hypertension was 36.17%. The results showed that there were significant differences between the hypertension group and the non-hypertension group in multiple variables (P < 0.05), including race, bmi, smoking, alcohol consumption, diabetes, stroke, creatinine, and GHR levels. Specifically, the proportion of individuals who regularly smoked and drank alcohol was higher in the hypertension group, and the prevalence of diabetes and stroke also increased significantly. In addition, the proportion of middle-aged and elderly blacks and whites with a BMI of more than 30 kg/m² was higher in the hypertension group. The creatinine and GHR levels in the hypertension group also showed a higher trend compared with the non-hypertension group. Details were shown in Table 1 . Table 1 Baseline characteristics of the study population based on hypertension Characteristic Overall (n = 17109) No hypertension (n = 10920) hypertension (n = 6189) p-value Age (years) 47.65 ± 16.90 42.97 ± 15.75 57.37 ± 14.94 < 0.001 Gender,n (%) 0.321 Male 8,410 (49.16%) 5,400 (49.45%) 3,010 (48.63%) Female 8,699 (50.84%) 5,520 (50.55%) 3,179 (51.37%) Race, n (%) < 0.001 Mexican American 2,804 (16.39%) 2,050 (18.77%) 754 (12.18%) Other Hispanic 1,557 (9.10%) 1,057 (9.68%) 500 (8.08%) White 7,685 (44.92%) 4,790 (43.86%) 2,895 (46.78%) Black 3,405 (19.90%) 1,861 (17.04%) 1,544 (24.95%) Other Race 1,658 (9.69%) 1,162 (10.65%) 496 (8.01%) BMI,kg/m2 28.92 ± 6.84 27.84 ± 6.31 31.18 ± 7.32 < 0.001 Smoked,n (%) < 0.001 Yes 7,844 (45.85%) 4,698 (43.02%) 3,146 (50.83%) No 9,265 (54.15%) 6,222 (56.98%) 3,043 (49.17%) Alcohol,n (%) < 0.001 Yes 12,581 (73.53%) 8,233 (75.39%) 4,348 (70.25%) No 4,528 (26.47%) 2,687 (24.61%) 1,841 (29.75%) Diabetes,n (%) < 0.001 Yes 2,261 (13.22%) 686 (6.28%) 1,575 (25.45%) No 14,848 (86.78%) 10,234 (93.72%) 4,614 (74.55%) Stroke,n (%) < 0.001 Yes 660 (3.86%) 167 (1.53%) 493 (7.97%) No 16,449 (96.14%) 10,753 (98.47%) 5,696 (92.03%) Creatinine,umol/L 78.24 ± 32.51 75.43 ± 21.16 84.07 ± 47.65 < 0.001 Total Cholesterol,mmol/L 5.02 ± 1.08 5.02 ± 1.06 5.02 ± 1.11 0.881 GHR 4.61 ± 2.27 4.32 ± 1.89 5.21 ± 2.81 < 0.001 As illustrated in Table 2 , the characteristics of the participants were stratified by GHR levels (Q1 4.98). GHR levels were associated with many factors, including age, gender, race, bmi, smoking, systolic and diastolic blood pressure, hypertension, diabetes, stroke, creatinine, and cholesterol(P < 0.05). Specifically, compared with the Q1 group with the lowest GHR levels, the Q2 and Q3 groups with higher GHR levels showed a higher prevalence of hypertension and systolic and diastolic blood pressure levels. Table 2 Features of the participants according to the tertile of the GHR Characteristic Q1 4.98 (n = 6219) p-value Age (years) 47.01 ± 17.33 46.57 ± 17.00 49.33 ± 16.24 < 0.001 Gender,n (%) < 0.001 Male 1,497 (27.94%) 2,804 (50.69%) 4,109 (66.07%) Female 3,861 (72.06%) 2,728 (49.31%) 2,110 (33.93%) Race, n (%) < 0.001 Mexican American 662 (12.35%) 933 (16.87%) 1,209 (19.44%) Other Hispanic 393 (7.34%) 508 (9.18%) 656 (10.55%) White 2,526 (47.15%) 2,398 (43.35%) 2,761 (44.40%) Black 1,246 (23.25%) 1,130 (20.43%) 1,029 (16.55%) Other Race 531 (9.91%) 563 (10.17%) 564 (9.06%) BMI,kg/m2 25.94 ± 5.64 28.92 ± 6.34 31.82 ± 7.11 < 0.001 Smoked,n (%) < 0.001 Yes 2,123 (39.62%) 2,477 (44.78%) 3,244(52.16%) No 3,235 (60.38%) 3,055 (55.22%) 2,975 (47.84%) Alcohol,n (%) 0.110 Yes 3,924 (73.24%) 4,038 (72.99%) 4,619 (74.27%) No 1,434 (26.76%) 1,494 (27.01%) 1,600 (25.73%) Systolic blood pressure, mmHg 118.96 ± 17.74 120.99 ± 16.31 125.04 ± 17.04 < 0.001 Diastolic blood pressure, mmHg 68.22 ± 11.83 69.66 ± 11.92 71.64 ± 12.78 < 0.001 Diabetes,n (%) < 0.001 Yes 180 (3.36%) 369 (6.67%) 1,712 (27.53%) No 5,178 (96.64%) 5,163 (93.33%) 4,507 (72.47%) Stroke,n (%) < 0.001 Yes 154 (2.87%) 186 (3.36%) 320 (5.15%) No 5,204 (97.13%) 5,346 (96.64%) 5,899 (94.85%) hypertension < 0.001 Yes 1,486 (27.73%) 1,886 (34.10%) 2,817 (45.30%) No 3,872 (72.27%) 3,646 (65.90%) 3,402 (54.70%) Creatinine,umol/L 73.67 ± 32.11 78.31 ± 28.28 82.62 ± 35.95 < 0.001 Total Cholesterol,mmol/L 5.19 ± 1.02 4.99 ± 1.05 4.88 ± 1.13 < 0.001 3.2 Multivariate logistic regression analysis In this study, we constructed three regression models to explore the relationship between GHR and hypertension (Table 3). Univariate logistic regression analysis showed that GHR was an independent risk factor for hypertension and was positively correlated with hypertension (OR = 1.20, 95%CI: 1.17–1.22, P < 0.01). After adjusting for potential confounders, including race, gender, age, and BMI, this positive correlation persisted in Model1 (OR = 1.11, 95%CI: 1.09–1.14, P < 0.01). Further adjustment for lifestyle factors (smoking and alcohol consumption) and diabetes, stroke, total cholesterol and creatinine, the positive correlation in Model 2 remained significant (OR = 1.06, 95%CI: 1.03–1.09, P < 0.01). In addition, to comprehensively evaluate the relationship between GHR and hypertension and test its robustness, a tertile (Q1 4.98) analysis of GHR was conducted. Compared with the Q1 group with the lowest GHR, the risk of hypertension in the Q2 and Q3 groups increased significantly. Specifically, in the unadjusted crude model, the risks of the Q2 and Q3 groups were 48% and 37% higher, respectively; after adjusting for confounding factors, the risks of the Q2 and Q3 groups in Model 1 were 41% and 81% respectively, and in Model 2 they were 35% and 48% respectively, all of which were statistically significant (P < 0.05). Table 3. Association of GHR with risk of hypertension. Characteristic Crude Model Model 1 Model 2 OR(95% CI) P value OR(95% CI) P value OR(95% CI) P value GHR 1.20(1.17, 1.22) <0.001 1.11(1.09, 1.14) <0.001 1.06(1.03, 1.09) <0.001 Categories Q1 Ref - Ref - Ref - Q2 1.48(1.32, 1.67) <0.001 1.41(1.22, 1.63) <0.001 1.35(1.117 1.56) <0.001 Q3 2.37(2.11, 2.66) <0.001 1.81(1.54, 2.12) <0.001 1.48(1.25, 1.74) <0.001 P for trend - <0.001 - <0.001 - <0.001 Crude model, unadjusted; Model 1, adjusted for age, gender, race, body mass index, Model 2, adjusted for age, gender, race, body mass index, smoking, alcohol consumption, diabetes, stroke, total cholesterol and creatinine, GHR, Fasting blood sugar to high-density lipoprotein cholesterol ratio. 3.3 Subgroup analysis Through subgroup analysis, this study evaluated the potential relationship between GHR and hypertension in different demographic variables. The results showed that the correlation between GHR and hypertension was consistent in the subgroups of gender and BMI, and no significant interaction was found (P > 0.05). However, there were significant differences in the relationship between GHR and hypertension in the subgroups of race, age, and diabetes status (P < 0.05). The results were detailed in Fig. 2 . 3.3 RCS and threshold effect analyses To further explore the relationship between GHR and hypertension, RCS analysis and threshold effect analysis were conducted. The results showed a significant non-linear relationship between GHR and hypertension (P-non-linear < 0.0001). The threshold effect analysis showed that when the GHR level exceeded 2.36, it was significantly associated with the risk of hypertension (OR = 1.16, 95% CI: 1.14–1.17, P < 0.001). In contrast, GHR levels below 2.36 were positively associated with the risk of hypertension, but the correlation was not significant (OR = 1.09, 95% CI: 0.60-2.00, P = 0.78). In addition, the likelihood ratio test results also support this threshold effect (P < 0.001). The detailed results were shown in Fig. 3 and Table 4 . Table 4 Threshold effect analysis OR(95%CI) P value Logistic regression analysis on each side of the inflection point 2.36 1.16 (1.14, 1.17) < 0.001 Log likelihood ratio test < 0.001 3.3 Diagnostic value of the GHR for hypertension To evaluate the diagnostic value of GHR for hypertension, ROC curve analysis was used to assess its diagnostic accuracy. The results showed that the AUC of GHR was 0.6034 (95%CI: 0.5946–0.6124) in the unadjusted Crude model. To further evaluate the diagnostic accuracy of GHR after considering confounding factors, the age, gender, race, and BMI variables were adjusted in Model 1, and the results showed that the AUC of GHR significantly increased to 0.7934 (95%CI: 0.7865–0.8005). In Model 2, variables such as diabetes, stroke, creatinine, cholesterol, smoking, and alcohol consumption were further added, and the AUC of GHR was further improved to 0.8076 (95%CI: 0.8013–0.8114). The results were shown in Fig. 4 and Table 5 . Table 5 Diagnostic accuracy of theGHR for identifying hypertension Model AUC 95%CI low 95%CI_upp SP(%) SE(%) Youden’s index Crude 0.6034 0.5946 0.6124 56.49 59.33 0.1582 Model1 0.7934 0.7865 0.8005 66.30 79.21 0.4551 Model2 0.8076 0.8013 0.8114 69.59 77.36 0.4695 Crude model, unadjusted; Model 1, adjusted for age, gender, race, body mass index, Model 2, adjusted for age, gender, race, body mass index, smoking, alcohol consumption, diabetes, stroke, total cholesterol and creatinine, GHR, Fasting blood sugar to high-density lipoprotein cholesterol ratio. 4 Discussion This study is the first to explore the potential relationship between GHR and hypertension. The results demonstrate a significant positive correlation between GHR and hypertension. Even after adjusting for various covariates, including age, race, gender, smoking, drinking, diabetes, stroke, creatinine, cholesterol, and BMI, the positive correlation between GHR and hypertension remains consistent. Further subgroup analysis also confirms the stability of this relationship. In addition, the study reveals a nonlinear relationship between GHR and hypertension. Threshold effect analysis suggests that when the GHR level is > 2.36, it helps assess the potential hypertension risk. Significantly, ROC analysis shows that even with the influence of multidimensional confounding factors, GHR's evaluation of hypertension remains effective. The relationship between hypertension and inflammation, as well as oxidative stress, was the subject of extensive research 20 , 21 . Oxidative stress functioned as a core driver, producing reactive oxygen species (ROS), such as superoxide anion (O 2− ) and hydroxyl radicals (·OH), which directly damaged vascular endothelial function, inhibited nitric oxide (NO) synthesis, and promoted the release of endothelin-1 (ET-1), leading to vasoconstriction and increased peripheral resistance 22 , 23 . Additionally, ROS activated NADPH oxidase (Nox4) and mitochondrial dysfunction, promoting calcium overload, proliferation, and collagen deposition in vascular smooth muscle cells, and accelerated vascular remodeling 24 , 25 . In addition, an oxidative stress environment could induce an inflammatory response. ROS promoted the release of inflammatory factors by activating the NF-κB pathway, recruited monocyte macrophages to infiltrate the vascular wall, formed lipid plaques, and exacerbated local inflammation 23 . In turn, inflammatory factors amplified oxidative stress and vascular damage by upregulating the NLRP3 inflammasome and the RAS system 26 . Blood sugar level was strongly correlated with oxidative stress, the elevation of which could exacerbate oxidative stress 7 . HDL had anti-inflammatory and antioxidant properties 27 . By inhibiting the NF-κB pathway, it reduced the release of inflammatory factors, such as TNF-α and IL-6, thereby mitigating the negative impact of the inflammatory microenvironment surrounding the blood vessels on blood pressure 28 . HDL mitigated oxidative stress by scavenging reactive oxygen species (ROS) and reduced damage to the vascular endothelium. It also promoted nitric oxide (NO) production, which directly dilated vascular smooth muscle and lowered peripheral resistance 28 , 29 . Therefore, GHR levels may have served as a reflection of the body's inflammatory and oxidative stress responses. Research had demonstrated a notable correlation between elevated GHR levels and an augmented risk of fatal cardiovascular events 17 . It had been imperative to assess the association between GHR and hypertension and to investigate the potential implications of GHR in the context of hypertension, with the aim of facilitating its prevention and management. In this study, there was a significant correlation between GHR and hypertension, and this relationship remained consistent even after adjusting for various covariates. These findings suggested that GHR could serve as a reliable indicator for predicting the risk of hypertension. Subgroup analysis also revealed that this correlation persisted across different age groups, races, genders, smoke, drink, and diabetes statuses, indicating that the relationship between GHR and hypertension might be universal. Nonetheless, subgroup interaction tests indicated substantial disparities in terms of race, age, diabetes status, and smoking. These disparities imply that, while there is a persistent correlation between GHR and hypertension, the efficacy in diverse populations might be impacted by additional factors. Previous studies had shown that diabetes was a risk factor for high blood pressure 30 . Previous studies showed that age was an important factor in the risk of developing hypertension 31 . Analysis showed that the risk significantly increased in people over 60. This might have been related to age-related physiological changes and the accumulation of chronic diseases 32 . Subgroup analysis revealed that GHR also significantly influenced the risk of hypertension in subgroups with smoking and drinking habits. Compared to non-smokers, smokers had a slightly lower risk of hypertension. This seems to contradict the belief that smoking increases the risk of hypertension 33 . A cross-sectional study showed that smoking could lead to metabolic disorders of blood glucose and blood lipids, affecting blood glucose, high-density lipoprotein, and low-density lipoprotein levels 34 , 35 , 36 . It caused an increase in blood glucose and low-density lipoprotein levels, and a decrease in high-density lipoprotein levels, which might explain the results of the subgroup analysis of GHR in smokers. We used RCS analysis to explore the relationship between GHR and hypertension further. The results showed a non-linear correlation between GHR and hypertension, with a turning point at 2.36. It was observed that when the GHR level exceeded 2.36, GHR exerted a comparatively greater influence on hypertension. This indicated that a GHR value of 2.36 was an important cut-off point. Beyond this value, the relationship between GHR and the risk of hypertension became more significant. Furthermore, a ROC analysis was conducted to evaluate the diagnostic accuracy of GHR for hypertension. The results demonstrated that GHR exhibited high predictive efficiency in both univariate and multivariate models. Therefore, GHR could be used as an effective predictor of the prevalence of hypertension. Monitoring GHR could improve the early detection of hypertension, identify individuals with potential hypertension risk, and allow for proactive preventive measures to be taken early to reduce the risk of hypertension, such as changing lifestyle habits, like smoking and drinking 37 . This study had several notable advantages. Firstly, the data came from the NHANES sample, which used a rigorous stratified multi-stage sampling method to ensure the reliability and representativeness of the data. Secondly, the study fully considered various confounding factors, including gender, age, race, alcohol consumption, and smoking. However, this study also had some limitations. Firstly, a cross-sectional study design could not determine causality, and more prospective studies would be needed to further validate. Secondly, some of the data in the NHANES were collected through questionnaires, which might have information bias, affecting the accuracy of the association between GHR and hypertension. Despite the aforementioned limitations, this study was the first to explore the correlation between GHR and hypertension, providing a new perspective for research in this field and laying the groundwork for discovering potential preventive strategies and interventions for hypertension. 5 Conclusion This study showed a positive correlation between GHR and hypertension. Further RCS curve analysis revealed a non-linear relationship between GHR and hypertension, with an inflection point of 2.36. In addition, ROC analysis showed that GHR had a high predictive efficiency. Therefore, by regularly measuring GHR levels, clinicians might have been able to identify potential risks early, take timely interventions, and reduce disease progression. More research was needed in the future to provide more accurate evidence. Declarations Funding The authors declare that financial support was received for the research, authorship, and/or publication of this article.This work was supported by The 2022 National Key Research and Development Program "Modernization of Traditional Chinese Medicine" Key Special Project of China (2022YFC3501202) and Innovative Research Group Project of Hunan Provincial Natural Science Fund (2024JJ1007). Author Contribution SL: Writing–review and editing, Writing–original draft, Investigation, Data acquisition. JZ: Methodology, Software. LY : Formal analysis, Visualization. JL: Formal analysis, Visualization. YD:Writing–review and editing, Supervision, Project administration, Methodology, Funding acquisition. Acknowledgement The authors sincerely thank the NHANES staff and participants. In addition, the authors thank the BioRender(https://www.biorender.com). Data Availability Publicly available datasets were analyzed in this study. This data can be found here: https://wwwn.cdc.gov/nchs/nhanes. Ethics statement The studies were approved by National Center for Health Statistics Ethics Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. References Kario, K., Okura, A., Hoshide, S. & Mogi, M. The Who Global Report 2023 On Hypertension Warning the Emerging Hypertension Burden in Globe and its Treatment Strategy. Hypertens. Res. 47 , 1099-1102 (2024). Zhang, X. et al. The Neutrophil-to-Lymphocyte Ratio is Associated with All-Cause and Cardiovascular Mortality Among Individuals with Hypertension. Cardiovasc. Diabetol. 23 , 117 (2024). Harbaoui, B. et al. Cumulative Effects of Several Target Organ Damages in Risk Assessment in Hypertension. Am. J. Hypertens. 29 , 234-244 (2016). Lu, Q. et al. Association of Lifestyle Factors and Antihypertensive Medication Use with Risk of All-Cause and Cause-Specific Mortality Among Adults with Hypertension in China. Jama Netw. Open 5 , e2146118 (2022). Wierzejska, E. et al. A Global Perspective On the Costs of Hypertension: A Systematic Review. Arch. Med. Sci. 16 , 1078-1091 (2020). Jia, G. & Sowers, J. R. Hypertension in Diabetes: An Update of Basic Mechanisms and Clinical Disease. Hypertension 78 , 1197-1205 (2021). Gonzalez, P., Lozano, P., Ros, G. & Solano, F. Hyperglycemia and Oxidative Stress: An Integral, Updated and Critical Overview of their Metabolic Interconnections. Int. J. Mol. Sci. 24 (2023). Camargo, L. L., Rios, F. J., Montezano, A. C. & Touyz, R. M. Reactive Oxygen Species in Hypertension. Nat. Rev. Cardiol. 22 , 20-37 (2025). Griendling, K. K. et al. Oxidative Stress and Hypertension. Circ. Res. 128 , 993-1020 (2021). Zhang, X., Miao, J., Song, Y. & Miao, M. The Effects of Okra Consumption On Glycemic Parameters and Lipid Profile in Adults: A Systematic Review and Meta-Analysis. Food Sci. Nutr. 12 , 10049-10058 (2024). Chen, W., Liu, L. & Hu, F. Efficacy of Vitamin D Supplementation On Glycaemic Control in Type 2 Diabetes: An Updated Systematic Review and Meta-Analysis of Randomized Controlled Trials. Diabetes Obes. Metab. 26 , 5713-5726 (2024). Tripp, L. & Sawchuk, L. A. The Emergence of a Suburban Penalty During the 1918/19 Influenza Pandemic in Malta: The Role of a Marketplace, Railway, and Measles. Plos Glob Public Health 3 , e2167 (2023). Chang, H. C. et al. Hdl and Associated Factors Stratified by Sex and Menopausal Status: Results From a Community-Based Survey in Taiwan. Oncotarget 9 , 16354-16367 (2018). Kim, D. S. et al. Effects of Dietary Components On High-Density Lipoprotein Measures in a Cohort of 1,566 Participants. Nutr. Metab. 11 , 44 (2014). Wu, B. et al. Correlation Between Gallstones and Fasting Blood Glucose to Serum High-Density Lipoprotein Cholesterol Ratio Among American Adults. Front. Med. 12 , 1528613 (2025). Jin, X., Xu, J. & Weng, X. Correlation Between Ratio of Fasting Blood Glucose to High Density Lipoprotein Cholesterol in Serum and Non-Alcoholic Fatty Liver Disease in American Adults: A Population Based Analysis. Front. Med. 11 , 1428593 (2024). Deng, S. et al. Association of Fasting Blood Glucose to High-Density Lipoprotein Cholesterol Ratio with Short-Term Outcomes in Patients with Acute Coronary Syndrome. Lipids Health Dis. 21 , 17 (2022). Zhang, H., Xu, Y. & Xu, Y. The Value of the Platelet/High-Density Lipoprotein Cholesterol Ratio in Predicting Depression and its Cardiovascular Disease Mortality: A Population-Based Observational Study. Front. Endocrinol. 15 , 1402336 (2024). Chen, J. et al. Association Between Platelet to High-Density Lipoprotein Cholesterol Ratio (Phr) and Hypertension: Evidence From Nhanes 2005-2018. Lipids Health Dis. 23 , 346 (2024). Caminiti, R. et al. The Potential Effect of Natural Antioxidants On Endothelial Dysfunction Associated with Arterial Hypertension. Front. Cardiovasc. Med. 11 , 1345218 (2024). Cheang, I. et al. Associations of Inflammation with Risk of Cardiovascular and All-Cause Mortality in Adults with Hypertension: An Inflammatory Prognostic Scoring System. J. Inflamm. Res. 15 , 6125-6136 (2022). Munteanu, C., Galaction, A. I., Onose, G., Turnea, M. & Rotariu, M. Harnessing Gasotransmitters to Combat Age-Related Oxidative Stress in Smooth Muscle and Endothelial Cells. Pharmaceuticals 18 , 344 (2025). Valaitienė, J. & Laučytė-Cibulskienė, A. Oxidative Stress and its Biomarkers in Cardiovascular Diseases. Artery Res. 30 , 18 (2024). Zhao, Z. et al. Echs1-Nox4 Interaction Suppresses Rotenone-Induced Dopaminergic Neurotoxicity through Inhibition of Mitochondrial Ros Production. Free. Radic. Biol. Med. (2025). Jie, H. et al. Interplay Between Energy Metabolism and Nadph Oxidase-Mediated Pathophysiology in Cardiovascular Diseases. Front. Pharmacol. 15 , 1503824 (2024). Mo, D. G. et al. The Effect of Nlrp3 Inflammasome On Cardiovascular Prognosis in Patients with Acute Coronary Syndrome. Sci. Rep. 15 , 1187 (2025). Jia, C. et al. High-Density Lipoprotein Anti-Inflammatory Capacity and Incident Cardiovascular Events. Circulation 143 , 1935-1945 (2021). Endo, Y., Fujita, M. & Ikewaki, K. Hdl Functions-Current Status and Future Perspectives. Biomolecules 13 (2023). Xu, S., Huang, X., Wang, Y., Liu, J. & Zhang, W. The Effect of Dual Antioxidant Modification On Oxidative Stress Resistance and Anti-Dysfunction of Non-Split Hdl and Recombinant Hdl. Int. J. Biol. Macromol. 278 , 134632 (2024). Nasser, S. M. et al. Prevalence of Hypertension and Associated Factors: A Cross-Sectional Study in Riyadh, Saudi Arabia. Bmc Health Serv. Res. 25 , 351 (2025). Association of Age and Blood Pressure Among 3.3 Million Adults: Insights From China Peace Million Persons Project. J. Hypertens. 39 , 1143-1154 (2021). Kim, J. H. & Thiruvengadam, R. Hypertension in an Ageing Population: Diagnosis, Mechanisms, Collateral Health Risks, Treatments, and Clinical Challenges. Ageing Res. Rev. 98 , 102344 (2024). Gao, N. et al. Assessing the Association Between Smoking and Hypertension: Smoking Status, Type of Tobacco Products, and Interaction with Alcohol Consumption. Front. Cardiovasc. Med. 10 , 1027988 (2023). Sousa, I. R. et al. Relationship Between Smoking and Lipid Profile in Four Primary Health Care Units: A Research Study. Cureus 16 , e69172 (2024). Rouland, A. et al. Smoking and Diabetes. Ann. Endocrinol. (Paris). 85 , 614-622 (2024). Yuan, S. & Larsson, S. C. A Causal Relationship Between Cigarette Smoking and Type 2 Diabetes Mellitus: A Mendelian Randomization Study. Sci. Rep. 9 , 19342 (2019). Ding, C. et al. Association Between Nontraditional Lipid Profiles and Peripheral Arterial Disease in Chinese Adults with Hypertension. Lipids Health Dis. 19 , 231 (2020). 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. 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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-6213126","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":448649107,"identity":"2b2924a3-4068-47cd-b817-f1d0733f8907","order_by":0,"name":"Shengping Luo","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shengping","middleName":"","lastName":"Luo","suffix":""},{"id":448649108,"identity":"fcf9fa06-09e9-4bec-ac65-17556ad14deb","order_by":1,"name":"Jiayu Zhu","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"Zhu","suffix":""},{"id":448649109,"identity":"01bbcb61-8894-404a-b689-2b3929a2c4a5","order_by":2,"name":"Le Yang","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Yang","suffix":""},{"id":448649110,"identity":"e2ca192e-dd5d-4648-9b38-e501bbeb5c4a","order_by":3,"name":"Jing Liu","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu","suffix":""},{"id":448649111,"identity":"b00c3e22-c5b4-4609-b774-09cecfb44646","order_by":4,"name":"Yihui Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDCCAyDC4AADG3sPTCiBWC08Z0jSAiIlcojUwne89/BrnoI7iX2Sb4895qnZxsDPnmPA8HMHbi2SZ86lWfMYPEtsk85LN+Y5dptBsueNAWPvGdxaDG7kmBnzGBwGaskxk+ZtuA0SMWBmbMOj5f4bqBbJMxAt9gS13OAxfgzWIsEDtUWCgBbJMzlmjHMMDhu38eSYSc45dptH4syzgoO9eLTwHT9j/OHNn8Oy89vPmEm8qbktx9+evPHBTzxagIBNigeJB2YfwKuBgYH54w8CKkbBKBgFo2CEAwDm11KkHZU6bAAAAABJRU5ErkJggg==","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yihui","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2025-03-12 14:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6213126/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6213126/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81632681,"identity":"24fc94a8-9b6e-4db2-9a5d-f22c15949c66","added_by":"auto","created_at":"2025-04-29 11:48:44","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92035,"visible":true,"origin":"","legend":"\u003cp\u003eScreening flowchart\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6213126/v1/181ce6b8a65f9f7eb5514fe0.jpeg"},{"id":81632683,"identity":"ff6982cf-b823-4b4d-a766-5f9ca3aeac72","added_by":"auto","created_at":"2025-04-29 11:48:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":392328,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between GHR and hypertension\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6213126/v1/2fce27bb9ae8f69eb796a441.png"},{"id":81633820,"identity":"555d6c9c-b47f-4fcf-b634-f7151f719f25","added_by":"auto","created_at":"2025-04-29 11:56:44","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83567,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between GHR and hypertension\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6213126/v1/cbc6c7d3c11715dd75185a94.jpeg"},{"id":81632041,"identity":"deb2f9d4-f961-4051-8891-5b99c28457f6","added_by":"auto","created_at":"2025-04-29 11:40:44","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126219,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve of the GHR for identifying hypertension, Crude model, unadjusted; Model 1, adjusted for age, gender, race, body mass index, Model 2, adjusted for age, gender, race, body mass index,, smoking, alcohol consumption, diabetes, stroke, total cholesterol and creatinine.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6213126/v1/ebaad881b4a82d745c58c6ae.jpeg"},{"id":85742029,"identity":"fcad555f-3980-4d7f-8c33-4ae4c6810972","added_by":"auto","created_at":"2025-07-01 09:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1634266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6213126/v1/8846e488-8656-4485-a918-96419cad7fab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association fasting blood sugar to high-density lipoprotein cholesterol ratio and hypertension: Analysis from the National Health and Nutrition Examination Survey (2003– 2018)","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHypertension is a common chronic disease worldwide. It is a significant risk factor for cardiovascular disease and represents a serious threat to public health. Hypertension has emerged as a significant global public health concern, impacting over a billion individuals worldwide\u003csup\u003e \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003c/sup\u003e. The persistent nature of hypertension exerts a deleterious effect on vital organs, including the heart, brain, and kidneys, leading to severe conditions such as chronic heart failure, cerebral hemorrhage, and Chronic renal failure\u003csup\u003e \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e \u003c/sup\u003e.Research findings suggest that in 2019, hypertension was responsible for 10\u0026nbsp;million deaths\u003csup\u003e \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003c/sup\u003e, a statistic that serves as a clarion call for heightened global health awareness. Moreover, the economic burden of hypertension is substantial; in the United States, the annual cost of dealing with hypertension and its related diseases is as high as \u003cspan\u003e$\u003c/span\u003e131 billion\u003csup\u003e \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e \u003c/sup\u003e, which poses a severe challenge to the socio-economic system.With the acceleration of global population aging and the continuous rise in obesity rates, the incidence of hypertension and related diseases is also on the rise. In the future, the prevention and control of hypertension will face even greater tasks and challenges.\u003c/p\u003e \u003cp\u003eRegular monitoring is a simple and effective evaluation method for patients diagnosed with hypertension. However, there is currently a lack of practical assessment tools for individuals who are potentially at risk. Therefore, it is imperative to identify predictive factors to facilitate early screening and risk stratification in the management of hypertension. Recognizing these factors enables earlier intervention and preventive measures, thereby reducing the incidence of hypertension and its complications. Hypertension has been demonstrated to be closely linked to blood glucose levels\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Hyperglycemic conditions have been shown to disrupt the balance between oxidants and antioxidants within cells, leading to increased oxidative stress\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, which plays a central role in the pathogenesis of hypertension\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Studies show that increased levels of reactive oxygen species (ROS) lead to heightened oxidative stress, which causes endothelial injury, vascular dysfunction, systemic inflammation, and accelerates the process of arteriosclerosis, ultimately triggering the onset of hypertension\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFasting blood sugar, a measure of blood glucose levels, is a reliable indicator. However, its stability is relatively poor and can be influenced by certain foods and medications, such as okra and vitamin D\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. High-density lipoprotein (HDL) is a protective lipoprotein that plays a positive role in resisting inflammation and oxidative stress\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, various factors have been demonstrated to influence HDL levels, including gender, alcohol consumption, smoking, and eating habits\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The ratio of fasting blood glucose to high-density lipoprotein (GHR) has been identified as a comprehensive indicator for evaluating the dynamic changes of information by combining fasting blood glucose and high-density lipoprotein levels. Previous studies have found an association between GHR and diseases such as gallstones, acute coronary syndrome, and non-alcoholic fatty liver\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, the relationship between GHR and hypertension remains to be thoroughly studied.\u003c/p\u003e \u003cp\u003eThis study is based on data from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018 and systematically explores the relationship between GHR and hypertension, with particular attention to the impact of continuous changes in GHR on the risk of hypertension. This study aims to provide a more accurate and convenient method for assessing an individual's risk of hypertension. In addition, the research results will provide a scientific basis for healthcare professionals to develop effective prevention and treatment strategies, thereby optimizing the management of hypertension.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Survey data\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a health and nutrition survey conducted by the National Center for Health Statistics (NCHS) under the Centers for Disease Control and Prevention (CDC) of the United States. The survey is conducted every two years using a complex, stratified, multi-stage probability sampling method. It covers the non-institutionalized civilian population in all 50 states and the District of Columbia. The data from NHANES are made available to the public and encompass demographics, dietary habits, physical examinations, laboratory tests, and other relevant metrics. This data aims to comprehensively assess the health and nutritional status of adults and children in the United States.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study population\u003c/h2\u003e \u003cp\u003eThis study summarized data from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018, including 80,312 participants who underwent demographic assessments and laboratory tests and completed health status questionnaires. During the data collation process, some participants were excluded due to incomplete records, including participants who lacked information related to hypertension (n\u0026thinsp;=\u0026thinsp;30,331), participants who lacked fasting blood glucose or high-density lipoprotein levels (n\u0026thinsp;=\u0026thinsp;28,747), and other participants who had invalid data (n\u0026thinsp;=\u0026thinsp;4,899). The final research cohort comprised a total of 17,109 participants. The detailed exclusion process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Evaluation of GHR\u003c/h2\u003e \u003cp\u003eThe biochemical data collected from the participants in this study were sourced from the NHANES laboratory data section. The GHR index was used as the exposure variable, with the calculation formula being fasting blood glucose (mmol/L)/high-density lipoprotein cholesterol level (mmol/L).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Definition of hypertension\u003c/h2\u003e \u003cp\u003e According to the \u0026ldquo;Guidelines for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults\u0026rdquo; published by the American College of Cardiology/American Heart Association (ACC/AHA) in 2017, hypertension was defined as a systolic blood pressure of \u0026ge;\u0026thinsp;140 mmHg and/or a diastolic blood pressure of \u0026ge;\u0026thinsp;90 mmHg. Blood pressure measurements were conducted by professionals following standardized procedures. After resting for 5 minutes, the subjects' blood pressure was measured three times using a mercury sphygmomanometer. Based on these three measurements, individuals with an average systolic blood pressure of \u0026ge;\u0026thinsp;140 mmHg and/or an average diastolic blood pressure of \u0026ge;\u0026thinsp;90 mmHg were classified as having hypertension. Additionally, even if the average systolic blood pressure was \u0026lt;\u0026thinsp;140 mmHg and diastolic blood pressure was \u0026lt;\u0026thinsp;90 mmHg, individuals who met the criteria of \u0026ldquo;doctor-diagnosed hypertension\u0026rdquo; or \u0026ldquo;were taking antihypertensive medications\u0026rdquo; were also classified as having hypertension. This method was consistent with the standards used in previous studies to identify individuals with hypertension in the NHANES.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Covariates\u003c/h2\u003e \u003cp\u003eIn order to investigate the independent correlation between GHR and hypertension, this study screened potential covariates related to hypertension based on previous research, including gender, race, age, body mass index (BMI), smoking status, alcohol consumption, diabetes, stroke, total cholesterol and creatinine\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Race was further subdivided into Mexican-Americans, other Hispanics, non-Hispanic whites, non-Hispanic blacks, and other races. In the context of lifestyle variables, smoking status was defined as having smoked at least 100 cigarettes in their lifetime. In contrast, alcohol consumption was defined as having consumed at least 12 drinks in the past year. Detailed information regarding the data acquisition process could be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo ensure the accuracy and reliability of the data, NHANES employed a stratified, multi-stage probability sampling design, combined with appropriate weighting methods. In this study, all statistical analyses utilized sampling weights to reflect the national representativeness of the research subjects. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables were expressed as percentages. A multivariate logistic regression model was employed to examine the association between the growth hormone receptor (GHR) and hypertension. Three models were constructed: the crude model, which included only GHR; model 1, which further adjusted for gender, age, race, and body mass index (BMI); and model 2, which was based on model 1 and additionally included smoking status, alcohol consumption, diabetes, stroke, total cholesterol, urine protein, and creatinine for comprehensive adjustment. Furthermore, the study investigated the potential impact of gender, race, age, BMI, diabetes, and stroke on the relationship between GHR and hypertension through subgroup analysis. Interaction terms were incorporated to assess heterogeneity among subgroups, and the results were presented as a forest plot. Additionally, the Restricted Cubic Spline (RCS) analysis method was employed to explore a possible nonlinear relationship between GHR and hypertension. In the event that a nonlinear relationship was identified, the inflection point was further precisely identified, and the threshold effect was evaluated using the likelihood ratio test. Concurrently, the diagnostic accuracy of GHR in the crude model and model 2 was assessed using Receiver Operating Characteristic (ROC) analysis. All statistical analyses were completed using R software (version 4.4.2), and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of participants\u003c/h2\u003e \u003cp\u003eThis study included 17,109 participants, with 8,410 men and 8,699 women. The prevalence of hypertension was 36.17%. The results showed that there were significant differences between the hypertension group and the non-hypertension group in multiple variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including race, bmi, smoking, alcohol consumption, diabetes, stroke, creatinine, and GHR levels. Specifically, the proportion of individuals who regularly smoked and drank alcohol was higher in the hypertension group, and the prevalence of diabetes and stroke also increased significantly. In addition, the proportion of middle-aged and elderly blacks and whites with a BMI of more than 30 kg/m\u0026sup2; was higher in the hypertension group. The creatinine and GHR levels in the hypertension group also showed a higher trend compared with the non-hypertension group. Details were shown in 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\u003eBaseline characteristics of the study population based on hypertension\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17109)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo hypertension \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10920)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypertension \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6189)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.97\u0026thinsp;\u0026plusmn;\u0026thinsp;15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.37\u0026thinsp;\u0026plusmn;\u0026thinsp;14.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,410 (49.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,400 (49.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,010 (48.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,699 (50.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,520 (50.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,179 (51.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,804 (16.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,050 (18.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e754 (12.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,557 (9.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,057 (9.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500 (8.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,685 (44.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,790 (43.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,895 (46.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,405 (19.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,861 (17.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,544 (24.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,658 (9.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,162 (10.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e496 (8.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI,kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.18\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoked,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,844 (45.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,698 (43.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,146 (50.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,265 (54.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,222 (56.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,043 (49.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,581 (73.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,233 (75.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,348 (70.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,528 (26.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,687 (24.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,841 (29.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,261 (13.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e686 (6.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,575 (25.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,848 (86.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,234 (93.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,614 (74.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e660 (3.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (1.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e493 (7.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,449 (96.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,753 (98.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,696 (92.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine,umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.24\u0026thinsp;\u0026plusmn;\u0026thinsp;32.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.43\u0026thinsp;\u0026plusmn;\u0026thinsp;21.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.07\u0026thinsp;\u0026plusmn;\u0026thinsp;47.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;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\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the characteristics of the participants were stratified by GHR levels (Q1\u0026thinsp;\u0026lt;\u0026thinsp;3.62, Q2:3.62\u0026ndash;4.98, Q3\u0026thinsp;\u0026gt;\u0026thinsp;4.98). GHR levels were associated with many factors, including age, gender, race, bmi, smoking, systolic and diastolic blood pressure, hypertension, diabetes, stroke, creatinine, and cholesterol(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, compared with the Q1 group with the lowest GHR levels, the Q2 and Q3 groups with higher GHR levels showed a higher prevalence of hypertension and systolic and diastolic blood pressure levels.\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\u003eFeatures of the participants according to the tertile of the GHR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eQ1\u0026thinsp;\u0026lt;\u0026thinsp;3.62\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5358)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eQ2 3.62\u0026ndash;4.98\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5532)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eQ3\u0026thinsp;\u0026gt;\u0026thinsp;4.98\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6219)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e47.01\u0026thinsp;\u0026plusmn;\u0026thinsp;17.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e46.57\u0026thinsp;\u0026plusmn;\u0026thinsp;17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e49.33\u0026thinsp;\u0026plusmn;\u0026thinsp;16.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e1,497 (27.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e2,804 (50.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e4,109 (66.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e3,861 (72.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e2,728 (49.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e2,110 (33.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e662 (12.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e933 (16.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e1,209 (19.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e393 (7.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e508 (9.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e656 (10.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e2,526 (47.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e2,398 (43.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e2,761 (44.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e1,246 (23.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e1,130 (20.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e1,029 (16.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e531 (9.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e563 (10.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e564 (9.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI,kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e25.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e28.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e31.82\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoked,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e2,123 (39.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e2,477 (44.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e3,244(52.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e3,235 (60.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e3,055 (55.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e2,975 (47.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlcohol,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e3,924 (73.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e4,038 (72.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e4,619 (74.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e1,434 (26.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e1,494 (27.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e1,600 (25.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e118.96\u0026thinsp;\u0026plusmn;\u0026thinsp;17.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e120.99\u0026thinsp;\u0026plusmn;\u0026thinsp;16.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e125.04\u0026thinsp;\u0026plusmn;\u0026thinsp;17.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e68.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e69.66\u0026thinsp;\u0026plusmn;\u0026thinsp;11.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e71.64\u0026thinsp;\u0026plusmn;\u0026thinsp;12.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiabetes,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e180 (3.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e369 (6.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e1,712 (27.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e5,178 (96.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e5,163 (93.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e4,507 (72.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStroke,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e154 (2.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e186 (3.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e320 (5.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e5,204 (97.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e5,346 (96.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e5,899 (94.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ehypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e1,486 (27.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e1,886 (34.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e2,817 (45.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e3,872 (72.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e3,646 (65.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e3,402 (54.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCreatinine,umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e73.67\u0026thinsp;\u0026plusmn;\u0026thinsp;32.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e78.31\u0026thinsp;\u0026plusmn;\u0026thinsp;28.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e82.62\u0026thinsp;\u0026plusmn;\u0026thinsp;35.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal Cholesterol,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e5.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;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 \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multivariate logistic regression analysis\u003c/h2\u003e \u003cp\u003eIn this study, we constructed three regression models to explore the relationship between GHR and hypertension (Table\u0026nbsp;3). Univariate logistic regression analysis showed that GHR was an independent risk factor for hypertension and was positively correlated with hypertension (OR\u0026thinsp;=\u0026thinsp;1.20, 95%CI: 1.17\u0026ndash;1.22, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). After adjusting for potential confounders, including race, gender, age, and BMI, this positive correlation persisted in Model1 (OR\u0026thinsp;=\u0026thinsp;1.11, 95%CI: 1.09\u0026ndash;1.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Further adjustment for lifestyle factors (smoking and alcohol consumption) and diabetes, stroke, total cholesterol and creatinine, the positive correlation in Model 2 remained significant (OR\u0026thinsp;=\u0026thinsp;1.06, 95%CI: 1.03\u0026ndash;1.09, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, to comprehensively evaluate the relationship between GHR and hypertension and test its robustness, a tertile (Q1\u0026thinsp;\u0026lt;\u0026thinsp;3.62, Q2: 3.62\u0026ndash;4.98, Q3\u0026thinsp;\u0026gt;\u0026thinsp;4.98) analysis of GHR was conducted. Compared with the Q1 group with the lowest GHR, the risk of hypertension in the Q2 and Q3 groups increased significantly. Specifically, in the unadjusted crude model, the risks of the Q2 and Q3 groups were 48% and 37% higher, respectively; after adjusting for confounding factors, the risks of the Q2 and Q3 groups in Model 1 were 41% and 81% respectively, and in Model 2 they were 35% and 48% respectively, all of which were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAssociation of GHR with risk of hypertension.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15.0186%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24.0892%;\"\u003e\n \u003cp\u003eCrude Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.8736%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 25.5762%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6654%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6543%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2193%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1524%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0186%;\"\u003e\n \u003cp\u003eGHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e1.20(1.17, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6654%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6543%;\"\u003e\n \u003cp\u003e1.11(1.09,\u0026nbsp;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2193%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e1.06(1.03, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1524%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0186%;\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6654%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2193%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0186%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6654%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6543%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2193%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1524%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0186%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e1.48(1.32, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6654%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6543%;\"\u003e\n \u003cp\u003e1.41(1.22, 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2193%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e1.35(1.117 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1524%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0186%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e2.37(2.11, 2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6654%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6543%;\"\u003e\n \u003cp\u003e1.81(1.54, 2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2193%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e1.48(1.25, 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1524%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0186%;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6654%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6543%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.2193%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4238%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1524%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCrude model, unadjusted; Model 1, adjusted for age, gender, race, body mass index, Model 2, adjusted for age, gender, race, body mass index, smoking, alcohol consumption, diabetes, stroke, total cholesterol and creatinine, GHR, Fasting blood sugar to high-density lipoprotein cholesterol ratio.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subgroup analysis\u003c/h2\u003e \u003cp\u003eThrough subgroup analysis, this study evaluated the potential relationship between GHR and hypertension in different demographic variables. The results showed that the correlation between GHR and hypertension was consistent in the subgroups of gender and BMI, and no significant interaction was found (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, there were significant differences in the relationship between GHR and hypertension in the subgroups of race, age, and diabetes status (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results were detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 RCS and threshold effect analyses\u003c/h2\u003e \u003cp\u003eTo further explore the relationship between GHR and hypertension, RCS analysis and threshold effect analysis were conducted. The results showed a significant non-linear relationship between GHR and hypertension (P-non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The threshold effect analysis showed that when the GHR level exceeded 2.36, it was significantly associated with the risk of hypertension (OR\u0026thinsp;=\u0026thinsp;1.16, 95% CI: 1.14\u0026ndash;1.17, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, GHR levels below 2.36 were positively associated with the risk of hypertension, but the correlation was not significant (OR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 0.60-2.00, P\u0026thinsp;=\u0026thinsp;0.78). In addition, the likelihood ratio test results also support this threshold effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The detailed results were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect 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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLogistic regression analysis on each side of the inflection point\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.60, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 (1.14, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLog likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;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 \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Diagnostic value of the GHR for hypertension\u003c/h2\u003e \u003cp\u003eTo evaluate the diagnostic value of GHR for hypertension, ROC curve analysis was used to assess its diagnostic accuracy. The results showed that the AUC of GHR was 0.6034 (95%CI: 0.5946\u0026ndash;0.6124) in the unadjusted Crude model. To further evaluate the diagnostic accuracy of GHR after considering confounding factors, the age, gender, race, and BMI variables were adjusted in Model 1, and the results showed that the AUC of GHR significantly increased to 0.7934 (95%CI: 0.7865\u0026ndash;0.8005).\u003c/p\u003e \u003cp\u003eIn Model 2, variables such as diabetes, stroke, creatinine, cholesterol, smoking, and alcohol consumption were further added, and the AUC of GHR was further improved to 0.8076 (95%CI: 0.8013\u0026ndash;0.8114). The results were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic accuracy of theGHR for identifying hypertension\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI_upp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSP(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYouden\u0026rsquo;s index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCrude model, unadjusted; Model 1, adjusted for age, gender, race, body mass index, Model 2, adjusted for age, gender, race, body mass index, smoking, alcohol consumption, diabetes, stroke, total cholesterol and creatinine, GHR, Fasting blood sugar to high-density lipoprotein cholesterol ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study is the first to explore the potential relationship between GHR and hypertension. The results demonstrate a significant positive correlation between GHR and hypertension. Even after adjusting for various covariates, including age, race, gender, smoking, drinking, diabetes, stroke, creatinine, cholesterol, and BMI, the positive correlation between GHR and hypertension remains consistent. Further subgroup analysis also confirms the stability of this relationship. In addition, the study reveals a nonlinear relationship between GHR and hypertension. Threshold effect analysis suggests that when the GHR level is \u0026gt;\u0026thinsp;2.36, it helps assess the potential hypertension risk. Significantly, ROC analysis shows that even with the influence of multidimensional confounding factors, GHR's evaluation of hypertension remains effective.\u003c/p\u003e \u003cp\u003eThe relationship between hypertension and inflammation, as well as oxidative stress, was the subject of extensive research\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Oxidative stress functioned as a core driver, producing reactive oxygen species (ROS), such as superoxide anion (O\u003csup\u003e2\u0026minus;\u003c/sup\u003e) and hydroxyl radicals (\u0026middot;OH), which directly damaged vascular endothelial function, inhibited nitric oxide (NO) synthesis, and promoted the release of endothelin-1 (ET-1), leading to vasoconstriction and increased peripheral resistance\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Additionally, ROS activated NADPH oxidase (Nox4) and mitochondrial dysfunction, promoting calcium overload, proliferation, and collagen deposition in vascular smooth muscle cells, and accelerated vascular remodeling\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In addition, an oxidative stress environment could induce an inflammatory response. ROS promoted the release of inflammatory factors by activating the NF-κB pathway, recruited monocyte macrophages to infiltrate the vascular wall, formed lipid plaques, and exacerbated local inflammation\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In turn, inflammatory factors amplified oxidative stress and vascular damage by upregulating the NLRP3 inflammasome and the RAS system\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Blood sugar level was strongly correlated with oxidative stress, the elevation of which could exacerbate oxidative stress\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. HDL had anti-inflammatory and antioxidant properties\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. By inhibiting the NF-κB pathway, it reduced the release of inflammatory factors, such as TNF-α and IL-6, thereby mitigating the negative impact of the inflammatory microenvironment surrounding the blood vessels on blood pressure\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. HDL mitigated oxidative stress by scavenging reactive oxygen species (ROS) and reduced damage to the vascular endothelium. It also promoted nitric oxide (NO) production, which directly dilated vascular smooth muscle and lowered peripheral resistance\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, GHR levels may have served as a reflection of the body's inflammatory and oxidative stress responses. Research had demonstrated a notable correlation between elevated GHR levels and an augmented risk of fatal cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. It had been imperative to assess the association between GHR and hypertension and to investigate the potential implications of GHR in the context of hypertension, with the aim of facilitating its prevention and management.\u003c/p\u003e \u003cp\u003eIn this study, there was a significant correlation between GHR and hypertension, and this relationship remained consistent even after adjusting for various covariates. These findings suggested that GHR could serve as a reliable indicator for predicting the risk of hypertension. Subgroup analysis also revealed that this correlation persisted across different age groups, races, genders, smoke, drink, and diabetes statuses, indicating that the relationship between GHR and hypertension might be universal. Nonetheless, subgroup interaction tests indicated substantial disparities in terms of race, age, diabetes status, and smoking. These disparities imply that, while there is a persistent correlation between GHR and hypertension, the efficacy in diverse populations might be impacted by additional factors. Previous studies had shown that diabetes was a risk factor for high blood pressure\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Previous studies showed that age was an important factor in the risk of developing hypertension\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Analysis showed that the risk significantly increased in people over 60. This might have been related to age-related physiological changes and the accumulation of chronic diseases\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Subgroup analysis revealed that GHR also significantly influenced the risk of hypertension in subgroups with smoking and drinking habits. Compared to non-smokers, smokers had a slightly lower risk of hypertension. This seems to contradict the belief that smoking increases the risk of hypertension\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. A cross-sectional study showed that smoking could lead to metabolic disorders of blood glucose and blood lipids, affecting blood glucose, high-density lipoprotein, and low-density lipoprotein levels\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. It caused an increase in blood glucose and low-density lipoprotein levels, and a decrease in high-density lipoprotein levels, which might explain the results of the subgroup analysis of GHR in smokers.\u003c/p\u003e \u003cp\u003eWe used RCS analysis to explore the relationship between GHR and hypertension further. The results showed a non-linear correlation between GHR and hypertension, with a turning point at 2.36. It was observed that when the GHR level exceeded 2.36, GHR exerted a comparatively greater influence on hypertension. This indicated that a GHR value of 2.36 was an important cut-off point. Beyond this value, the relationship between GHR and the risk of hypertension became more significant. Furthermore, a ROC analysis was conducted to evaluate the diagnostic accuracy of GHR for hypertension. The results demonstrated that GHR exhibited high predictive efficiency in both univariate and multivariate models. Therefore, GHR could be used as an effective predictor of the prevalence of hypertension. Monitoring GHR could improve the early detection of hypertension, identify individuals with potential hypertension risk, and allow for proactive preventive measures to be taken early to reduce the risk of hypertension, such as changing lifestyle habits, like smoking and drinking\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study had several notable advantages. Firstly, the data came from the NHANES sample, which used a rigorous stratified multi-stage sampling method to ensure the reliability and representativeness of the data. Secondly, the study fully considered various confounding factors, including gender, age, race, alcohol consumption, and smoking. However, this study also had some limitations. Firstly, a cross-sectional study design could not determine causality, and more prospective studies would be needed to further validate. Secondly, some of the data in the NHANES were collected through questionnaires, which might have information bias, affecting the accuracy of the association between GHR and hypertension. Despite the aforementioned limitations, this study was the first to explore the correlation between GHR and hypertension, providing a new perspective for research in this field and laying the groundwork for discovering potential preventive strategies and interventions for hypertension.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study showed a positive correlation between GHR and hypertension. Further RCS curve analysis revealed a non-linear relationship between GHR and hypertension, with an inflection point of 2.36. In addition, ROC analysis showed that GHR had a high predictive efficiency. Therefore, by regularly measuring GHR levels, clinicians might have been able to identify potential risks early, take timely interventions, and reduce disease progression. More research was needed in the future to provide more accurate evidence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that financial support was received for the research, authorship, and/or publication of this article.This work was supported by The 2022 National Key Research and Development Program \"Modernization of Traditional Chinese Medicine\" Key Special Project of China (2022YFC3501202) and Innovative Research Group Project of Hunan Provincial Natural Science Fund (2024JJ1007).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSL: Writing\u0026ndash;review and editing, Writing\u0026ndash;original draft, Investigation, Data acquisition. JZ: Methodology, Software. LY : Formal analysis, Visualization. JL: Formal analysis, Visualization. YD:Writing\u0026ndash;review and editing, Supervision, Project administration, Methodology, Funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors sincerely thank the NHANES staff and participants. In addition, the authors thank the BioRender(https://www.biorender.com).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://wwwn.cdc.gov/nchs/nhanes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies were approved by National Center for Health Statistics Ethics Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKario, K., Okura, A., Hoshide, S. \u0026amp; Mogi, M. The Who Global Report 2023 On Hypertension Warning the Emerging Hypertension Burden in Globe and its Treatment Strategy. \u003cem\u003eHypertens. Res.\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 1099-1102 (2024).\u003c/li\u003e\n\u003cli\u003eZhang, X. et al. The Neutrophil-to-Lymphocyte Ratio is Associated with All-Cause and Cardiovascular Mortality Among Individuals with Hypertension. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 117 (2024).\u003c/li\u003e\n\u003cli\u003eHarbaoui, B. et al. Cumulative Effects of Several Target Organ Damages in Risk Assessment in Hypertension. \u003cem\u003eAm. J. Hypertens.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 234-244 (2016).\u003c/li\u003e\n\u003cli\u003eLu, Q. et al. Association of Lifestyle Factors and Antihypertensive Medication Use with Risk of All-Cause and Cause-Specific Mortality Among Adults with Hypertension in China. \u003cem\u003eJama Netw. Open\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e2146118 (2022).\u003c/li\u003e\n\u003cli\u003eWierzejska, E. et al. A Global Perspective On the Costs of Hypertension: A Systematic Review. \u003cem\u003eArch. Med. Sci.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1078-1091 (2020).\u003c/li\u003e\n\u003cli\u003eJia, G. \u0026amp; Sowers, J. R. Hypertension in Diabetes: An Update of Basic Mechanisms and Clinical Disease. \u003cem\u003eHypertension\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 1197-1205 (2021).\u003c/li\u003e\n\u003cli\u003eGonzalez, P., Lozano, P., Ros, G. \u0026amp; Solano, F. Hyperglycemia and Oxidative Stress: An Integral, Updated and Critical Overview of their Metabolic Interconnections. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eCamargo, L. L., Rios, F. J., Montezano, A. C. \u0026amp; Touyz, R. M. Reactive Oxygen Species in Hypertension. \u003cem\u003eNat. Rev. Cardiol.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 20-37 (2025).\u003c/li\u003e\n\u003cli\u003eGriendling, K. K. et al. Oxidative Stress and Hypertension. \u003cem\u003eCirc. Res.\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, 993-1020 (2021).\u003c/li\u003e\n\u003cli\u003eZhang, X., Miao, J., Song, Y. \u0026amp; Miao, M. The Effects of Okra Consumption On Glycemic Parameters and Lipid Profile in Adults: A Systematic Review and Meta-Analysis. \u003cem\u003eFood Sci. Nutr.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 10049-10058 (2024).\u003c/li\u003e\n\u003cli\u003eChen, W., Liu, L. \u0026amp; Hu, F. Efficacy of Vitamin D Supplementation On Glycaemic Control in Type 2 Diabetes: An Updated Systematic Review and Meta-Analysis of Randomized Controlled Trials. \u003cem\u003eDiabetes Obes. Metab.\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 5713-5726 (2024).\u003c/li\u003e\n\u003cli\u003eTripp, L. \u0026amp; Sawchuk, L. A. The Emergence of a Suburban Penalty During the 1918/19 Influenza Pandemic in Malta: The Role of a Marketplace, Railway, and Measles. \u003cem\u003ePlos Glob Public Health\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, e2167 (2023).\u003c/li\u003e\n\u003cli\u003eChang, H. C. et al. Hdl and Associated Factors Stratified by Sex and Menopausal Status: Results From a Community-Based Survey in Taiwan. \u003cem\u003eOncotarget\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 16354-16367 (2018).\u003c/li\u003e\n\u003cli\u003eKim, D. S. et al. Effects of Dietary Components On High-Density Lipoprotein Measures in a Cohort of 1,566 Participants. \u003cem\u003eNutr. Metab.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 44 (2014).\u003c/li\u003e\n\u003cli\u003eWu, B. et al. Correlation Between Gallstones and Fasting Blood Glucose to Serum High-Density Lipoprotein Cholesterol Ratio Among American Adults. \u003cem\u003eFront. Med.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1528613 (2025).\u003c/li\u003e\n\u003cli\u003eJin, X., Xu, J. \u0026amp; Weng, X. Correlation Between Ratio of Fasting Blood Glucose to High Density Lipoprotein Cholesterol in Serum and Non-Alcoholic Fatty Liver Disease in American Adults: A Population Based Analysis. \u003cem\u003eFront. Med.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1428593 (2024).\u003c/li\u003e\n\u003cli\u003eDeng, S. et al. Association of Fasting Blood Glucose to High-Density Lipoprotein Cholesterol Ratio with Short-Term Outcomes in Patients with Acute Coronary Syndrome. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 17 (2022).\u003c/li\u003e\n\u003cli\u003eZhang, H., Xu, Y. \u0026amp; Xu, Y. The Value of the Platelet/High-Density Lipoprotein Cholesterol Ratio in Predicting Depression and its Cardiovascular Disease Mortality: A Population-Based Observational Study. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1402336 (2024).\u003c/li\u003e\n\u003cli\u003eChen, J. et al. Association Between Platelet to High-Density Lipoprotein Cholesterol Ratio (Phr) and Hypertension: Evidence From Nhanes 2005-2018. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 346 (2024).\u003c/li\u003e\n\u003cli\u003eCaminiti, R. et al. The Potential Effect of Natural Antioxidants On Endothelial Dysfunction Associated with Arterial Hypertension. \u003cem\u003eFront. Cardiovasc. Med.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1345218 (2024).\u003c/li\u003e\n\u003cli\u003eCheang, I. et al. Associations of Inflammation with Risk of Cardiovascular and All-Cause Mortality in Adults with Hypertension: An Inflammatory Prognostic Scoring System. \u003cem\u003eJ. Inflamm. Res.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 6125-6136 (2022).\u003c/li\u003e\n\u003cli\u003eMunteanu, C., Galaction, A. I., Onose, G., Turnea, M. \u0026amp; Rotariu, M. Harnessing Gasotransmitters to Combat Age-Related Oxidative Stress in Smooth Muscle and Endothelial Cells. \u003cem\u003ePharmaceuticals\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 344 (2025).\u003c/li\u003e\n\u003cli\u003eValaitienė, J. \u0026amp; Laučytė-Cibulskienė, A. Oxidative Stress and its Biomarkers in Cardiovascular Diseases. \u003cem\u003eArtery Res.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 18 (2024).\u003c/li\u003e\n\u003cli\u003eZhao, Z. et al. Echs1-Nox4 Interaction Suppresses Rotenone-Induced Dopaminergic Neurotoxicity through Inhibition of Mitochondrial Ros Production. \u003cem\u003eFree. Radic. Biol. Med.\u003c/em\u003e (2025).\u003c/li\u003e\n\u003cli\u003eJie, H. et al. Interplay Between Energy Metabolism and Nadph Oxidase-Mediated Pathophysiology in Cardiovascular Diseases. \u003cem\u003eFront. Pharmacol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1503824 (2024).\u003c/li\u003e\n\u003cli\u003eMo, D. G. et al. The Effect of Nlrp3 Inflammasome On Cardiovascular Prognosis in Patients with Acute Coronary Syndrome. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1187 (2025).\u003c/li\u003e\n\u003cli\u003eJia, C. et al. High-Density Lipoprotein Anti-Inflammatory Capacity and Incident Cardiovascular Events. \u003cem\u003eCirculation\u003c/em\u003e \u003cstrong\u003e143\u003c/strong\u003e, 1935-1945 (2021).\u003c/li\u003e\n\u003cli\u003eEndo, Y., Fujita, M. \u0026amp; Ikewaki, K. Hdl Functions-Current Status and Future Perspectives. \u003cem\u003eBiomolecules\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eXu, S., Huang, X., Wang, Y., Liu, J. \u0026amp; Zhang, W. The Effect of Dual Antioxidant Modification On Oxidative Stress Resistance and Anti-Dysfunction of Non-Split Hdl and Recombinant Hdl. \u003cem\u003eInt. J. Biol. Macromol.\u003c/em\u003e \u003cstrong\u003e278\u003c/strong\u003e, 134632 (2024).\u003c/li\u003e\n\u003cli\u003eNasser, S. M. et al. Prevalence of Hypertension and Associated Factors: A Cross-Sectional Study in Riyadh, Saudi Arabia. \u003cem\u003eBmc Health Serv. Res.\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 351 (2025).\u003c/li\u003e\n\u003cli\u003eAssociation of Age and Blood Pressure Among 3.3 Million Adults: Insights From China Peace Million Persons Project. \u003cem\u003eJ. Hypertens.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 1143-1154 (2021).\u003c/li\u003e\n\u003cli\u003eKim, J. H. \u0026amp; Thiruvengadam, R. Hypertension in an Ageing Population: Diagnosis, Mechanisms, Collateral Health Risks, Treatments, and Clinical Challenges. \u003cem\u003eAgeing Res. Rev.\u003c/em\u003e \u003cstrong\u003e98\u003c/strong\u003e, 102344 (2024).\u003c/li\u003e\n\u003cli\u003eGao, N. et al. Assessing the Association Between Smoking and Hypertension: Smoking Status, Type of Tobacco Products, and Interaction with Alcohol Consumption. \u003cem\u003eFront. Cardiovasc. Med.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1027988 (2023).\u003c/li\u003e\n\u003cli\u003eSousa, I. R. et al. Relationship Between Smoking and Lipid Profile in Four Primary Health Care Units: A Research Study. \u003cem\u003eCureus\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, e69172 (2024).\u003c/li\u003e\n\u003cli\u003eRouland, A. et al. Smoking and Diabetes. \u003cem\u003eAnn. Endocrinol. (Paris).\u003c/em\u003e \u003cstrong\u003e85\u003c/strong\u003e, 614-622 (2024).\u003c/li\u003e\n\u003cli\u003eYuan, S. \u0026amp; Larsson, S. C. A Causal Relationship Between Cigarette Smoking and Type 2 Diabetes Mellitus: A Mendelian Randomization Study. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 19342 (2019).\u003c/li\u003e\n\u003cli\u003eDing, C. et al. Association Between Nontraditional Lipid Profiles and Peripheral Arterial Disease in Chinese Adults with Hypertension. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 231 (2020).\u003c/li\u003e\n\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":"Fasting blood sugar to high-density lipoprotein cholesterol ratio, Hypertension, Cross-sectional study, NHANES, risk","lastPublishedDoi":"10.21203/rs.3.rs-6213126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6213126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHypertension is a chronic disease that poses a significant threat to human health worldwide. The ratio of fasting blood sugar to high-density lipoprotein cholesterol (GHR) is an emerging biomarker associated with various diseases. However, the relationship between GHR and hypertension was still unclear. This study aimed to explore the correlation between GHR and high blood pressure. The study conducted a comprehensive cross-sectional analysis based on data from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018. The study used a multivariate logistic regression model to evaluate the relationship between GHR and hypertension. It also verified the robustness of the results through subgroup analysis. In addition, the study used restricted cubic splines (RCS) to analyze the possible nonlinear relationship between GHR and hypertension, as well as to explore potential threshold effects. Finally, the study evaluated the predictive efficiency of GHR for hypertension through receiver operating characteristic (ROC) curve analysis. The results showed that, without adjusting for confounding factors, GHR was positively correlated with hypertension (OR\u0026thinsp;=\u0026thinsp;1.20, 95%CI\u0026thinsp;=\u0026thinsp;1.17\u0026ndash;1.22, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After fully adjusting for confounding factors (including gender, race, age, body mass index, smoking status, alcohol consumption, diabetes, stroke, total cholesterol, and creatinine), the positive correlation between GHR and hypertension still existed (OR\u0026thinsp;=\u0026thinsp;1.06, 95%CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.09, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analysis further showed that the association between GHR and hypertension remained consistent across different subgroups. In addition, ROC analysis revealed a nonlinear relationship between GHR and hypertension, with a turning point of 2.36. Furthermore, ROC analysis indicated that GHR demonstrated high predictive efficiency in univariate and multivariate-adjusted models. A positive correlation was demonstrated between GHR and hypertension. GHR had the potential to serve as a biological marker for hypertension, facilitating its prevention and diagnosis.\u003c/p\u003e","manuscriptTitle":"Association fasting blood sugar to high-density lipoprotein cholesterol ratio and hypertension: Analysis from the National Health and Nutrition Examination Survey (2003– 2018)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 11:40:40","doi":"10.21203/rs.3.rs-6213126/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":"d69b856c-01ab-464c-8914-489d89f9b8d3","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47743457,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":47743458,"name":"Health sciences/Medical research/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-07-01T08:54:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 11:40:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6213126","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6213126","identity":"rs-6213126","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}